This is the third major funding round for Cohesity, bringing the total investments in the company to more than $160 million
Source: Bloomberg
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Marketing may be a trillion-dollar industry, but its supply chain needs a makeover, says Microsoft's CMO.
Source: Ad Exchanger7305
Microsoft also announced Deep Learning and Machine Learning capabilities to support the next generation of enterprise-grade AI applications.
Source: BGR8439
Ten years ago, if you mentioned the term "artificial intelligence" in a boardroom there's a good chance you would have been laughed at. For most people it would bring to mind sentient, sci-fi machines such as 2001: A Space Odyssey's HAL or Star Trek's Data.
Source: Forbes8115
There has been a lot of interest in artificial intelligence and predictive learning systems - and with good reason. The systems provide a fast, powerful method to handle data analysis
Source: Forbes8991
Element Data, Inc., a decision support software platform that harnesses artificial intelligence and machine learning has acquired the technology assets and team of PV Cube, a Seattle area start-up.
Source: PR Newswire14265
Facebook's big bet on messenger bots was an oversell from the start. What's shaking out now is a more reserved and perhaps more useful idea of what a bot can be and how a business can use it.
Source: AD Exchanger7191
Maluuba, a Microsoft company working towards general artificial intelligence, recently released a new open dialogue dataset based on booking a vacation - specifically, finding flights and a hotel.
Source: Infoq16764
Zvelo, the leading provider of categorization and malicious detection data for web pages, devices and traffic, today announced the immediate availability of the Comprehensive Page-Level Traffic (CPT) dataset.
Source: Marketwired5808
Wolfram Research, Inc., creator of Mathematica, Wolfram|Alpha and the Wolfram Language, announces the integration of Wolfram technology with SolveBio, the operating system for precision medicine that enables biotech and pharmaceutical companies to leverage molecular information for therapeutic development.
Source: PR Newswire17520
Innovation in data processing and machine learning technology
Source: Google Cloud Platform23841
It can be said that an IT organization reflects the business from which it has grown. When it comes to the New York Times, this is definitely true. As a news organization, the company's collective journalistic head is always on the swivel, always racing towards the newest story.
Source: TheNewStack.6084
Technology is gradually taking over workplaces and that is one of the reasons why 'human workers' are becoming redundant.
Source: BGR11463
Credit reference agency Experian hold around 3.6 petabytes of data from people all over the world. This makes them an authority for banks and other financial institutions who want to know whether we represent a good investment, when we come to them asking for money.
Source: Forbes5826
Google, IBM, Microsoft and Amazon Web Services are all piling artificial intelligence capabilities onto their software stacks
Source: Computer World5007
Artificial intelligence offers feds the "tantalizing possibility" of increased speed, enhanced quality and lower costs, a Deloitte report says.
Source: FedTech9222
Exploring and applying machine learning algorithms to datasets that are too large to fit into memory is pretty common.
Source: Machinelearningmastery6972
In 1959, an IBM employee by the name of Arthur Samuel programmed a computer to play checkers against him. Over time, the program was able to collect data, strategise and win a game all by itself. And thus, machine learning was born.
Source: CSO5769
With the emergence of Artificial Intelligence (AI), Machine Learning (ML), Big Data, cloud computing and other concurrent new age technologies, the legacy IT companies and teams need to relook at their internal processes and structure.
Source: Financial Express6696
Financial services jobs go in and out of fashion. In 2001 equity research for internet companies was all the rage. In 2006, structuring collateralised debt obligations (CDOs) was the thing.
Source: efinancial Careers13497
According to industry headlines, the answers to many challenges facing credit unions today lie deep within their member data.
Source: blog.co-opfs4929
Google Sheets is getting smarter today. After adding the machine learning-powered "Explore" feature last year, which lets you ask natural language questions about your data, it's now expanding this feature to also automatically build charts for you. This means you can now simply ask Sheets to give you a "bar chart for fidget spinner sales" and it will automatically build one for you.
Source: TC6174
MLpAI can help deliver systems with more automation and less human intervention, but success requires a data strategy to deal with the complexity of real-world data.
Source: Gartner18894
"Artificial intelligence (AI) and machine learning (ML) have rapidly matured over the years and are already the norm in many fields, helping companies deploy smart systems of engagement to improve efficiency, enhance security, gain insights, and deliver superior customer experiences," writes Aan Chauhan.
Source: Congnizant8568
Demand for skilled data scientists continues to be sky-high, with IBM recently predicting that there will be a 28% increase in the number of employed data scientists in the next two years.
Source: Forbes23487
Alteryx, fresh off its IPO, introduces Alteryx Connect, a data catalog/governance product based off its previously undisclosed acquisition of Semanta. Alteryx is also announcing its acquisition of Yhat, a startup specializing in data science development, management, and deployment.
Source: ZDnet5907
Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies.
Source: HBR7440
What's on the horizon for Google AdWords? Columnist Frederick Vallaeys provides a behind-the-scenes look at some new AdWords features from a presentation at Google Marketing Next.
Source: Searchengineland6138
Recently, Nivida unveiled Volta, the most advanced data-center graphics-processing unit ever built. With 21.1 billion transistors and a massive 815 mm2 footprint, it will facilitate the next generation of artificial intelligence.
Source: Forbes5547
Microsoft's cloud-based virtual machine for big data analytics is now available in a version running on Windows Server 2016.
Source: Eweek6441
Data science and data analytics: people working in the tech field or other related industries probably hear these terms all the time, often interchangeably.
Source: Insidebigdata17565
Understanding machine learning & data science is easy. There are numerous open courses which you can take up right now and get started. But, acquiring in-depth knowledge of a subject requires extra effort.
Source: HOB Team22065
Machine learning algorithms can be divided into 3 broad categoriesâ??-â??supervised learning, unsupervised learning, and reinforcement learning.
Source: kdnuggets7497
We believe in the power of information. We also believe in markets and capitalism as a force for good. The two are inexorably linked, because markets don't work well without open access to reliable data and information
Source: SSIR.org5562
Artificial Intelligence (AI) is one of the most transformative forces of our times. While there may be debate whether AI will transform our world in good or evil ways, something we can all agree on is that AI would be nothing without big data.
Source: Forbes6798
One of the common problems people face in learning R is lack of a structured path. They don't know, from where to start, how to proceed, which track to choose?
Source: Analytics Vidya23307
Data science is just that - science. Specially trained individuals well-versed in how to break down big data sets and interpret their meaning are in high demand.
Source: TNW6360
There is more data out there than ever before, but organisations should be smart about how they use it.
Source: Gulfnews9414
How do Data Science and DevOps fit together? In this article, Richard Gall explains why integrating Data Science with your DevOps can lead to a better and smarter business.
Source: jaxenter6006
The skills of data experts are becoming outdated as the industry evolves. If you're looking to learn data science - quickly - then you're in luck.
Source: jaxenter5307
In the context of contemporary applications, it's hard to think of an application that doesn't use a database. From mobile to web to the desktop, every modern application relies on some form of a database.
Source: Forbes8193
Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.
Source: Kdnuggets9300
Google's Ground Truth team recently announced a new deep learning model for the automatic extraction of information from geo-located image files to improve Google Maps.
Source: Infoq8850
Scientists crunching numbers on infant deaths in the US are using data-analysis algorithms to try and find new ways to reduce the number of babies lost to SIDS each year, with Microsoft contributing free cloud hosting and software tools for their work.
Source: The Star5742
Think beyond the nosy neighbour to the corporations that want to utilise minutia of your life to sell products that you may or may not need. Corporations have always been interested in understanding consumer behaviour and been collecting data about users using their products or service.
Source: The Hindu5643
Today, Big Data gives us unprecedented insights and opportunities across all industries from healthcare to financial to manufacturing and more. But, it also raises concerns and questions that must be addressed.
Source: Forbes5838
What's the one thing that makes a great data leader?
Source: Data-informed4827
Data Scientists are in big demand! We review career pathways, relevant data science skills, and how you can learn them at no cost.
Source: KDnuggets6996
Spring. Rejuvenation. Rebirth. Everything's blooming. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right.
Source: KDnuggets7719
From gaining the right skills to acing your first interview, these resources can help put you on the right track
Source: PCworld5733
Compositions created using database of well-known pop, classical and jazz artists
Source: GATech5406
What are the top 5 skills needed to become a data scientist? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Source: Forbes7221
As data analytics matures from a science into a technology, companies are shifting their focus toward making products out of the advanced models generated by their data science organizations.
Source: A silicon ANGLE6438
Attribution technology still has a long way to go, but hopefully these technologies make that journey quicker.
Source: Inc5403
The growth of AI and large data sets pose great risks to privacy. Two top experts explain the issues to help your company manage this crucial part of the technology landscape.
Source: ZDnet5370
The race for artificial intelligence technology is on, and while tech giants like Google and Facebook snap up top talent to build out their own AI-powered products, a new startup has just raised a huge round of funding to help the rest of us
Source: TC6468
Data science is the theory and practice powering the data-driven transformations we are seeing across industry and society today. Artificial intelligence (AI), self-driving cars, and predictive analytics are just a few of the breakthroughs that have been made thanks to our ever-growing ability to collect and analyze data.
Source: Data Informed12561
Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars.
Source: KDnuggets12687
Facebook has a huge amount of data that is available for you to explore, you can do many things with this data. I will be sharing my experience with you on how you can use the Facebook Graph API for analysis with Python.
Source: KDnuggets9156
WHAT IS THE STATE OF SMART GOVERNMENT AND SMART CITIES TECHNOLOGY ADOPTION?
Source: Freebalance10671
Harvard will offer a Master of Science (SM) degree in Data Science beginning in fall of 2018. The new degree, under the joint academic leadership of the faculties of Computer Science in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and of Statistics in the Faculty of Arts and Sciences (FAS), will train students in the rapidly growing field of data science.
Source: HBR10236
US- and Bangalore-based Julia Computing, a provider of open source language for data science and machine learning, has received $4.6 million (Rs 30 crore) in seed funding from US-based investment firms General Catalyst and Founder Collective.
Source: VCC16863
Data Science is a comparatively new domain which most of us is not thorough with, agreed?
Source: Aircto5526
AI is defined by many terms that crop up everywhere and are often used interchangeably. Read through to better know the difference between AI, Machine Learning, and Deep Learning.
Source: Edgylabs8031
Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.
Source: medium7518
Imagine a business world where employees are faster and more productive - where they can make smarter decisions and have the time to focus on strategy and being creative. This is all a near-reality with continued breakthroughs in artificial intelligence (AI) capabilities. AI is at the tipping point of becoming the next great technological disruptor.
Source: Salesforce6585
By dispelling the myths surrounding this emerging technology, businesses will be able to realize its true potential.
Source: IT Proportal4176
With SmartAI, Datameer is addressing the last mile in putting machine learning to work in business intelligence.
Source: ZDnet6546
Retail investors do not always have adequate time to research opportunities for making money. Fortunately, the rise of big data and artificial intelligence (AI) is helping individual investors make more informed investment choices.
Source: Investopedia6384
The influence and impact of machine learning can be seen in everything from our morning coffee orders to the online banking apps we use.
Source: Datanami5856
The cyberthreat landscape evolves at breakneck speed. While cybercriminals are able to compromise a system in hours or minutes, the reaction of companies usually takes months or even years.
Source: Pandasecurity14208
What does it take to compete in a global arena in which retail and cloud are increasingly intertwined? Domain-specific data science and machine learning for the masses, according to Alibaba.
Source: ZDnet9021
A lot of people reach out to me. They needs jobs. But they are asking about school.
Source: Forbes5832
DataScience.com customers can now benefit from The Data Incubator's comprehensive data science training in the DataScience.com Platform.
Source: Global Newswire6756
AI, or artificial intelligence, has taken root in biotech. In this article, a contributor explores its newfound niches in the industry.
Source: Labiotech14433
There is no typecast for savvy AI businesses. They come in all sizes and represent an ever broadening swath of industry. Simply put, the era of artificial intelligence is remaking business as we know it.
Source: NVidia7428
Coming soon: 61st World Statistics Congress Marrakech, TDWI Anaheim, ICML Sydney, KDD-2017 Halifax, JupyterCon NYC, Big Data Innovation Summit Boston, and many more.
Source: KDnuggets15795
What size do big things start? Small Machine learning (ML) may well be The Next Big Thing, but it has yet to register in mainstream enterprise adoption. While breathless prognosticators proclaim 50 per cent of organisations lining up to magically transform themselves in 2017 with ML, more canny observers put the number closer to 15 per cent. And that's being generous.
Source: Theregister5604
There is a lot of excitement and some confusion across the ad industry around machine learning, and for good reason. The availability of cheap storage and processing has made sophisticated machine learning available to a much wider range of industries than what was available even five years ago. The media business has seen machine-learning solutions find homes in a wide variety of applications, from predicting how likely a user will click on an ad to classifying users in lookalike models and optimizing campaign delivery.
Source: Adexchanger25248
Siddhartha Dalal got his introduction to probabilistic analysis in the wake of the 1986 Space Shuttle Challenger disaster.
Source: Siliconangle7062
Machine learning with Big Data is, in many ways, different than "regular" machine learning. This informative image is helpful in identifying the steps in machine learning with Big Data, and how they fit together into a process of their own.
Source: KDnuggets20205
Want to be a data scientist? Third-level institutions and full-time education aren't the only way you can learn essential data science skills.
Source: Siliconrepublic10818
The chief data officer's next challenge may be to find storytellers who can explain machine learning for data science to decision-makers in the organization.
Source: Techtarget9951
Right now, many data science jobs require a Ph.D. Here's how companies can help employees who lack advanced degrees get on this career track.
Source: Techrepublic5421
So, you want to build a data science team? Here's some stuff to think about. Before long, just like this stock photo, you'll have a team of weird orange people with big bulbous heads, who can sit around a table looking at an enormous hologram of a simple bar chart.
Source: Econsultancy9177
Artificial intelligence-driven technology revolution is expected to impact all sectors. Some firms have already deployed AI solutions and are reaping the benefits
Source: Livemint12705
Artificial intelligence (AI) enables Siri to recognise your question, Google to correct your spelling, and tools such as Kinect to track you as you move around the room.
Source: Theconversation5844
In their HBR Big Idea feature, Erik Brynjolfsson and Andrew McAfee argue that AI and machine learning will soon become "general-purpose technologies," as significant as electricity or the internal combustion engine. They represent a landmark change in our technical capabilities and will power the next wave of economic growth.
Source: HBR12147
Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go. Sure, A.I. systems have mastered an array of games, from chess and Go to "Jeopardy" and poker, but the technology continues to struggle in the real world. Robots fall over while opening doors, prototype driverless cars frequently need human intervention, and nobody has yet designed a machine that can read reliably at the level of a sixth grader, let alone a college student. Computers that can educate themselves - a mark of true intelligence - remain a dream.
Source: NY Times7092
July 30 marks the United Nations' World Day Against Trafficking in Persons, a day focused on ending the criminal exploitation of children, women and men for forced labor or sex work
Source: Scientificamerican5487
Compared to the state-of-art, DeepSense provides an estimator with far smaller tracking error on the car tracking problem, and outperforms state-of-the-art algorithms on the HHAR and biometric user identification tasks by a large margin.
Source: Acolyer9345
Well-established enterprises like retailers or manufacturing companies now have an abundance of data at their disposal. Unfortunately, merely possessing vast amounts of raw data does not lead directly to increased efficiency or the rapid development of new revenue streams. Instead, everyone must now figure out exactly how to make this data work for them.
Source: Computerweekly13200
Demand for developers with data science skills continues to grow. Here's what you need to learn to break into a career in the field.
Source: Techrepublic6069
Many aspiring data scientists focus on doing Kaggle competitions as a way to build their portfolios. Kaggle is an excellent way to practice, but it should only be one of many avenues you use to work on data science projects.
Source: Dataquest5112
The words artificial intelligence (AI), machine learning (ML) and data visualization are everywhere right now. Both AI and ML have gained an immense role in defining the business world and have especially influenced the way we define the customer experience.
Source: Customerthink11382
Demand for this role: In most cases a machine learning engineer will partner with a data scientist so that their work will be synonymous with one another; therefore demand for these candidates arguably is extremely similar.
Source: Information Management8457
The technology is becoming commonplace in political campaigns, and some even claim it was crucial in delivering Donald Trump to the White House.
Source: Independent5142
There's no doubt chatbots are forever changing the way businesses operate and the way that marketers approach marketing. Not only do these virtual assistants have the ability to impact almost all aspects of a company, they're also great customer service tools that can provide assistance around the clock.
Source: Entrepreneur5610
Machine Learning and AI are often heralded as the future of, well, every industry ever. But what about the future of Machine Learning itself? Sebastian Raschka, applied machine learning and deep learning researcher at Michigan State University and the author of Packt's best-selling book Python Machine Learning, takes a look at what's changed the most in the last few years and what's next on the horizon - here's a hint, it's not robots taking over the world.
Source: CBR7887
As the integration of Artificial Intelligence, Machine Learning and Big Data in the workplace is becoming the new norm, concerns are increasing over how the technology could affect employee performance, job security and the adaptation and reliance of technology in the legal industry.
Source: Inside Counsel6612
Even though Walmart was founded in 1962, it's on the cutting edge when it comes to transforming retail operations and customer experience by using machine learning, the Internet of Things (IoT) and Big Data. In recent years, its patent applications, position as the second largest online retailer and investment in retail tech and innovation are just a few reasons they are among the retail leaders evolving to take advantage of tech to build their business and provide better service to their customers.
Source: Forbes16953
Although the laboratory earthquakes provide good simulations, its a very simplistic model. Real seismic data is a lot messier. There is ambient noise produced by human activity and the environment. Sounds from nearby faults also interfere with the signal pattern the researchers are looking for, making predictions more difficult.
Source: The Register6609
In retail, supply chain efficiency is essential. Inventory management, picking, packing and shipping are all time and resource-intensive processes which can have dramatic impact on a business's bottom line.
Source: Forbes8265
The most valuable contributors to machine learning are often generalists. Especially in 2017, there is a lot of hype around particular machine learning methods. Candidates who have learned how to use a certain deep learning package in an online course and are applying to jobs remind me of people in the 1990s, when there was similar hype around the web, who read the "Learn VBScript in 20 Days" kinds of books instead of learning the fundamentals of computer science.
Source: HuffPost13029
The state government on Wednesday announced it will establish a Centre of Excellence for Data Science and Artificial Intelligence (CoE-DS&AI), with Nasscom as the programme and implementation partner.
Source: ET Tech9198
There are many differing opinions on the impact of artificial intelligence (AI) on our worklives, from dazzling to dystopian.
Source: Forbes14211
Artificial intelligence (AI) is quickly becoming the next big thing for tech companies. Chipmakers, software companies, tech conglomerates, and hardware makers are all betting that AI will make our cars safer and our cities more efficient, help us find disease-fighting drugs faster, and improve our lives overall.
Source: The Motley Fool16767
Google acquired the British artificial-intelligence startup DeepMind in 2014 for a reported £400 million (roughly $525 million), a company its cofounder Demis Hassabis once described as aiming at "solving intelligence, and then using that to solve everything else.
Source: QZ22428
The big data tsunami bears exciting new profit potential; it also brings with it some daunting challenges, thanks to the General Data Protection Regulation's strict privacy rules, set to be enforced next year. Machine learning is the best weapon businesses have to maximize the bounty of big data and ward off the threats, according to Murthy Mathiprakasam (pictured), director of product marketing at Informatica Corp.
Source: siliconangle14556
"Data wrangling" was an interesting phrase to hear in the machine learning (ML) presentations at Microsoft Ignite. Interesting because data wrangling is from business intelligence (BI), not from artificial intelligence (AI). Microsoft understands ML incorporates concepts from both disciplines.
Source: Forbes7512
The artificial intelligence that will power future robots and content filters now has a new resource for understanding humans.
Source: QZ7926
The Delhi Police, by 2020, aspires to adopt "technology based policing" by using smart policing, artificial intelligence, and self-learning systems among other advanced technologies, it was announced on Friday.
Source: FirstPost6897
Alpha Go Zero is changing the game for how we solve big problems.
Source: Inc25044
While most of the attention for how artificial intelligence (AI), machine learning and big data can impact companies is focused on the business to consumer (B2C) space, business to business (B2B) companies need to pay attention or they risk their future success.
Source: Forbes9015
What is the role of machine learning in cyber security or networking security? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Source: Huffingtonpost4710
What is the role of machine learning in cyber security or networking security? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Source: Huffingtonpost8736
Usually when a new technology starts getting noticed, the subsequent hype in the industry is inevitable. The hype is partly created by the vendor and consulting community looking for new business, partly created by professionals in the industry wanting to keep up and comment on the latest trends, partly created by companies with the ambition to be seen moving with the times and becoming early adopters.
Source: CIO8436
Data lakes didn't quite pan out so now it's all about an abstraction layer and machine learning to save the day. Hopefully, machine learning can cleanse your data on the fly since humans have proved repeatedly they aren't meticulous enough.
Source: ZD Net7476
Infosys is seeking to leverage artificial intelligence and data analytics to help win more business from its own Fortune 500 clients and help run their businesses better
Source: Mint5826
Does it seem like the ability to find, hire and retain data scientists is a losing battle? Is spending $500.000-plus per year for a data scientist worth it? What is a data scientist anyway?
Source: IM10635
The combination of big data and machine learning can unlock the value of data you already have to gain a competitive edge for your business.
Source: IoT For All9000
The proliferation of digital technology in the last two decades has brought with it a slew of various cyber-threats, especially cybercrime, the most common example of which is data breaches.
Source: The Batt7308
In an era of great uncertainty and disruption for automotive manufacturers, Mercedes and its parent company Daimler are jumping in full throttle as leaders of the 4th Industrial Revolution.
Source: Forbes4563
In an era of great uncertainty and disruption for automotive manufacturers, Mercedes and its parent company Daimler are jumping in full throttle as leaders of the 4th Industrial Revolution.
Source: Forbes11640
The hope for the next generation of businesses is that the large quantities of data, which is accessible to the masses, can help in planning everything from better decision making to executing better marketing campaigns.
Source: Tech9225
Machine learning and big data are having a positive impact on customer experience, as well as producing extensive benefits for banks.
Source: Information Age10437
Tech is facing a talent gap, in the absence of formal data science degrees at major universities. The assumption that only computer science majors have a place in the new economy is a "very shallow view," CA Technologies CEO Mike Gregoire said.
Source: CNBC10953
Tech companies are constantly looking for an edge over the competition, and that usually means digging deep into data. That's good news for those who are mathematically inclined. If that sounds like you and you're looking to break into the Chicago tech scene, these open roles would be a good place to start.
Source: Builtinchicago9606
The skill most required today is the ability to come up with fundamental innovations in machine learning, and implement them to solve practical problems
Source: Hindustan Times7218
The age of Big Data has reached an all new high as disruptive and innovative digital technologies push businesses to adapt quickly in a rapidly changing consumer market.
Source: CXO Today7989
Really you want to upgrade your skills with the best Data Analytics, Development courses, to stand out in your industry? Now Big data, Data Science, Machine Learning, Deep Learning, Artificial Intelligence (AI), Analytics, Python, R, r-stats are the most trending and highly demanding subject in every sector for almost every industry.
Source: HOB Team7830
Really you want to upgrade your skills with the best Data Analytics , Development courses , to standout in your industry? Now Big data, Data Science, Machine Learning, Deep Learning, Artificial Intelligence (AI), Analytics, Python, R, r-stats are the most trending and highly demanding subject in every sector for almost every industry.
Source: HOB Team23262
Every industry can benefit from Big Data, IoT and AI, and that includes brewers. Dutch brewer Heineken has been a worldwide brewing leader for the last 150 years.
Source: Forbes7869
Machine learning is taking a big leap in Big Data stream. Today, Google predicts that you should leave now to catch a flight and Amazon recommends a book that you should read- are a few of the many machine learning usage instances that we come across in our lives daily.
Source: CIOL10155
Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn.
Source: Forbes46221
Among the biggest challenges CMOs-and all marketers- face is the transformation of data into actionable insights. Consequently, it's one of the hottest topics CMOs talk about (see here).
Source: Forbes6732
Healthcare outpaced all other industries in job growth for freelancers while finishing second to staffing when it comes to non-freelancers.
Source: Health Care IT News12669
Artificial intelligence and big data have formed a truly symbiotic relationship, and they need each other to ring to fruition what both are promising. If there are any lingering doubts that the fates of artificial intelligence and big data are intertwined, consider these recent quotes from two highly regarded thought leaders in this space:
Source: RTInsights4311
Artificial intelligence and big data have formed a truly symbiotic relationship, and they need each other to ring to fruition what both are promising. If there are any lingering doubts that the fates of artificial intelligence and big data are intertwined, consider these recent quotes from two highly regarded thought leaders in this space:
Source: RTInsights10194
Artificial intelligence is starting to impact nearly every aspect of our daily lives. Machine-learning algorithms, the technology behind contemporary AI, determine what content appears on our Facebook feed and what results are returned when we conduct a Google search. They power product recommendations on Amazon and Netflix, determine airline or event ticket pricing, and influence who receives marketing communications based on likelihood to buy a new product or cancel a service.
Source: Maclean's7335
There has been an increased appetite towards the adoption of artificial intelligence (AI) by Indian companies.This, in turn, may spike organisation spends on this tech, over the next 18 months, says an Intel India commissioned report, undertaken by the International Data Corporation (IDC)
Source: TOI9522
Machine learning engineers, data scientists, and Big Data engineers are among the top emerging jobs in technology. This is based off of a recently released report from LinkedIn.
Source: SD Times7656
Amazon Web Services (AWS) which is the cloud computing arm of Amazon, plans to get into Over-the-top (OTT) content providers market of India with their data analytics & artificial intelligence platform. Bikram Bedi, head of India region, AWS said that globally, and in India, media & entertainment has been a huge focus of the company. They work with OTT platforms or linear TV platforms like Voot, Hotstar, Netflix, Sony, Amazon Prime, Tata Sky etc.
Source: HOT Team10377
Indian AI Startups Raised About $87.85 Mn In 2017
Source: Inc4224447
Data visualization is the presentation of quantitative information in a graphical form. In other words, data visualizations turn large and small datasets into visuals that are easier for the human brain to understand and process.
Source: HOB13608
Layoffs and hiring freezes in Indian IT are likely to continue in 2018, but it's not as hopeless as it seems.
Source: QZ6114
The year 2018 will see a sharp increase in demand for professionals with skills in emerging technologies such as Artificial Intelligence (AI) and machine learning, even as people with capabilities in Big Data and Analytics will continue to be the most sought after by companies across sectors, say sources in the recruitment industry.
Source: The Hindu Business Line16404
Artificial intelligence (AI) and its subset, cognitive computing, have been subject to a fair share of debates, with concerns around machines overtaking, or maybe even replacing, the human workforce.
Source: Forbes9936
Accenture hosted an AI, Data Science & Big Data Open House at The Dock to showcase the data and analytics work it does, and open up a discussion about the challenges it faces in finding data-driven solutions.
Source: Silicon Republic7077
Automation, machine learning and other emerging technologies have made it necessary for IT employees to learn new skills.
Source: NDTV5760
Despite AI's promise across many industries, some companies still face implementation challenges.
Source: Tech Republic8913
Like other prominent AI researchers, the Nvidia team believes the techniques that drive this project will continue to improve in the months and years to come, generating significantly larger and more complex images.
Source: economictimes.indiatimes.com4281
Like other prominent AI researchers, the Nvidia team believes the techniques that drive this project will continue to improve in the months and years to come, generating significantly larger and more complex images.
Source: ET8205
A new article in Science talks about the impact of machine learning advancements on labor demands and the economy.
Source: newsclick10026
Verizon wants to give its enterprise customers more tools to automate threat detections on networks.
Source: Zdnet6306
Tesla has become a household name as a leader and pioneer in the electric vehicle market, but it also manufactures and sells advanced battery and solar panel technology.
Source: Forbes15921
Is data science too easy? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Source: Forbes7959
The answer to this question depends to a great extent on the role/industry/company combination. However, there are few general remarks that can be made. To start with, it is very important to complete an internship rather than do an "ML related summer research project" unless that research is done with respect to a lab at a university
Source: HOB8283
Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn. Data scientist roles have grown over 650% since 2012.
Source: Enterprise Irregulars7116
Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.
Source: Linkedin Blog13431
With digital transformation comes the daunting task of preparing a workforce for technologies like Big Data, Cloud, Artificial Intelligence (AI) and Internet of Things (IoT) that can address the massive demand coming from governments and businesses in India.
Source: Economictimes5946
A lower barrier to entry and a spike in the number of pilot projects involving machine learning will lead to various industries doubling its implementation this year and again in 2020, according to Deloitte Canada.
Source: ITbusiness.ca10905
The company said reskilling empowers Tech Mahindra associates with much needed learning opportunities to enhance their careers and stay relevant in the Digital Age.
Source: Economictimes9864
"Han the Robot" waits on stage before a discussion about the future of humanity in a demonstration of artificial intelligence at the RISE Technology Conference in Hong Kong on July 12, 2017. A new simulation of advanced AI reveals what society might look like if a superintelligence is introduced.
Source: Newsweek8769
Instead of rendering humans not as important in the workplace, AI will actually make us more capable and useful, with automated solutions freeing up more of our time and talent for higher-value thinking and problem solving, strategizing and creating innovative solutions.
Source: Betanews3969
As AI makes more resources more widely available, we will find less meaning in material wealth and more value in the activities that are uniquely human.
Source: Edweek5913
The core of most modern Machine Learning (ML) systems is automated neural networks (ANNs). The training of ANN's require large data sets.
Source: Forbes5949
These startups are applying artificial intelligence techniques to business intelligence, big data, cybersecurity, APM, autonomous vehicles, healthcare and more.
Source: Datamation12156
Artificial Intelligence (AI) forms part of the Fourth Industrial Revolution, which is expected to fundamentally alter the way people live. We are in the midst of this revolution, and its end result is unknown.
Source: Mediaupdate10644
Such is the speed of technological change that research shows four out of five executives feel overwhelmed and underprepared for the challenges of the next five years. Hardly surprising - for some, the next five years will see more change than the last 20.
Source: The Drum5280
Machine learning is a fundamentally new technology that can create immense value to humankind. At the same time, it will challenge society. Not to try to understand how it works would be irresponsible.
Source: blog.networks.nokia11769
Bennett Borden, chief data scientist at the Philadelphia firm of Drinker, Biddle & Reath, discusses his take on the evolution of legal analytics.
Source: Law.com5757
A deep neural network model developed by Alibaba has scored higher than humans in a reading comprehension test, paving the way for bots to replace people in customer service jobs
Source: Scmp7725
Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems.
Source: Toptal14340
Thousands of students and technology enthusiasts from Mumbai experienced Artificial Intelligence (AI) for the first time after humanoid Sophia 'spoke' at Indian Institute of Technology Bombay (IIT-B) on New Year's Eve.
Source: Hindustantimes5472
The program is the first-of-its-kind that offers industry-recognized certification in Management Program in Data Visualization and Executive Program in Big Data and Machine Learning.
Source: Aninews6831
Over the next decade, artificial intelligence will have a profound impact on all industries, introducing efficiencies and innovative catalysts.
Source: Morningstar5676
Imaging analytics backed by machine learning can accurately predict renal survival time in patients with chronic kidney disease.
Source: Healthitanalytics6999
Artificial intelligence and machine learning are eating up workloads at IT help desks, in cybersecurity, and other IT tasks, stirring significant concern over the long-term impact AI will have on jobs even in the IT industry.
Source: Itworld7269
See how big data and cloud computing can be used together to manage the enormous amounts of data that are being generated on a daily basis.
Source: Dzone11268
It is largely data that has determined success, failure and change in the insurance business. However, today is different. The advent of big data technology, advanced data analytics & machine learning are changing the game entirely.
Source: HOB8991
Just consider the fact, In an average winter, Kentucky's Department of Transportation spend somewhere between $45 million to $75 million on snow removal & road salting. However, during the harsh winter of 2014-15
Source: HOB5964
Data protection platforms are a key element of data supply chains.Yet data supply chains present unique challenges.
Source: Forbes9567
Artificial intelligence (AI) is rapidly advancing, thanks to ever-more-powerful computing, massive growth in the availability of digital data and increasingly sophisticated algorithms. The world's largest technology firms are investing billions to develop their AI capabilities, and companies across industries, from travel to real estate to fashion, are racing to bring AI-enabled services to market.
Source: BSR9027
There are new technologies being introduced everyday and regular advances in managing and utilizing consumer data is also happening. Resultantly, it is becoming harder to stay up to date with all of them. Technology is moving faster than marketers can figure how best to make use of it.
Source: HOB5982
AI-enhanced real estate industry could also mean agents advertising homes using virtual reality, where users can experience a home just like they experienced a new BMW on Snapchat, interacting with the property through the smartphone in their hands.
Source: Forbes9444
Artificial intelligence can help synthesize and analyze the large volumes of information provided by big data initiatives. The two are different, but they work well together.
Source: Tech Target13464
It's been a decade since D.J. Patil, LinkedIn's former head of data products, coined the term "data science." And already there are thousands of data scientists working in different companies.
Source: Techgenix17664
Machine Learning Engineer is now the fastest-growing job position in the U.S. according to LinkedIn's 2017 U.S. Emerging Jobs Report.
Source: Standuply12996
Interviews are scary as shit. You sit across the table from someone who has the power to grant you income and measure of security. They hold your future in their hands. You have to make them like you, trust you, think you are smart.
Source: Github.io15375
It is a question that perplexes many an upcoming research student - which IIT is best suited for machine learning and data science research. Globally and on home turf, IITs are recognized as Tier-1 engineering institutions that produce a steady stream of talent in computer science, software engineering, Database and Information Systems and other related domains.
Source: HOB22056
Today, the importance of machine learning and big data to businesses cannot be overemphasized; both are revolutionizing business operations and consistently providing lots of new opportunities.
Source: Entrepreneur6807
Businesses have been relying on search and big data analytics for many years to gain insight into their data. In recent years, these technologies have evolved rapidly and now incorporate machine learning and artificial intelligence,
Source: CMSWire9846
"We need to look to the past in the face of modern innovations in machine learning, robotics, artificial intelligence, big data, and beyond," says the economist.
Source: MIT Edu5355
The Australian Securities Exchange (ASX) has published its financial results for the first half of the year. The results showed AU$230.5 million in after-tax profit and an increase of AU$11.1 million over the year.
Source: HOB6192
When Terah Lyons arrives at the Flywheel Coffee Roasters in San Francisco's Haight-Ashbury, she is greeted so enthusiastically that she laughs with surprise. But this can't have been the first such welcome. Even if you don't know who she is, the ease and poise with which she walks and the warmth of her smile make it hard not to be struck by her presence. And as if on cue, from the speaker overhead comes Alicia Keys' hit song "You Don't Know My Name."
Source: Ozy7632
The biggest event on the data science community calendar is the one that showcases women in the field.
Source: Forbes6201
A recent survey of over 16,000 data professionals showed that the most used platforms/resources included Kaggle, Online courses and Stack Overflow Q&A.
Source: Businessoverbroadway16674
University of Michigan-Ann Arbor, University of London, University of Illinois-Urbana Champaign, Imperial College London, and Arizona State University partner with Coursera to build online degrees of the future
Source: Business Wire16968
Coders and job aspirants alike are making a beeline to data science training shops in Bengaluru and Hyderabad, and to open online courses, in the hopes of updating their skills and landing jobs with comfortable salaries. But is the training enough?
Source: Livemint6465
Data science is one of the most lucrative fields to work in today. Learn the basics and get a fatter paycheck with this machine learning class.
Source: Dailydot10293
Cloudera, Inc., (NYSE: CLDR), the modern platform for machine learning and analytics, optimized for the cloud, announced Cloudera Altus with SDX, the first machine learning and analytics Platform-as-a-Service (PaaS), built with a shared data catalog providing the business context of that data.
Source: PR Newswire7386
We live in a world with unprecedented amounts of stats, facts, and figures, and it's slightly overwhelming. And in order to make that data useful, companies need to hire analysts who know how to sift through it and find meaningful patterns. According to GlassDoor, a typical data scientist earns about $120,000 per year, meaning this is the kind of career that can get you living that good life.
Source: Mashable10314
Release 5.1 of Anaconda, the data science and machine learning platform, now includes Visual Studio Code as an IDE. This is part of a wider collaborative effort between Anaconda Inc. and Microsoft.
Source: Infoq16776
Employer demand for AI-related roles has more than doubled over the past three years, according to Indeed.
Source: Tech Republic12108
Preparing for an interview is not easy - naturally there is a large amount of uncertainty regarding the data science interview questions you will be asked. No matter how much work experience or technical skill you have, an interviewer can throw you off with a set of questions that you didn't expect. For a data science interview, an interviewer will ask questions spanning a wide range of topics, requiring strong technical knowledge and communication skills from the part of the interviewee. Your statistics, programming, and data modeling skills will be put to the test through a variety of questions and question styles - intentionally designed to keep you on your feet and force you to demonstrate how you operate under pressure. Preparation is a major key to success when in pursuit of a career in data science.
Source: Springboard30081
LinkedIn's 2017 U.S. Emerging Jobs Report-fittingly created with the power of data science-lists data science roles as one of the top emerging positions in the U.S. today, with 6.5X growth over the last five years.
Source: Springborad8172
Cracking any interview requires preparation and in the case of data science it is not restricted to performing well on the big day alone. An aspiring data scientist is expected to prepare across multiple fronts. Here, we provide you with a insight into the levels of preparation required and how to go about it.
Source: HOB13041
It's as good a time as any to keep yourself updated - especially for those who are in the ever-changing technology field. If you're interested in, or working as a professional in Data Science, Machine Learning and allied fields, we've compiled a list of top 11 books that are available free that you must catch up on gloomy rainy days.
Source: HOB20553
Data science requires the effective application of skills in a variety of machine learning areas and techniques. A recent survey by Kaggle, however, revealed that a limited number of data professionals possess competency in advanced machine learning skills.
Source: Businessover broadway19023
In a joint initiative with Google and Udacity, Bertelsmann, the international media, services and education company, is inviting the people 18 and older to apply for its "Udacity Data Science Scholarship Program," in which the company will provide 15,000 three-month Udacity online courses in descriptive statistics.
Source: HOB19449
Top machine learning writers on Quora give their advice on learning machine learning, including specific resources, quotes, and personal insights, along with some extra nuggets of information.
Source: kdnuggets.45561
Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science
Source: Kdnugget44829
Machine Learning, The New Frontier In Data Science The ability to draw insight from massive streams of data is becoming a competitive differentiator for enterprises. Gartner evaluated software vendors offering products that allow development and deployment of the data science workloads that deliver that insight.
Source: CRN11841
Are you a Data Scientist looking for a Job? Are you a Recruiter looking for a Data Scientist? If you answered yes or NO to this questions you need to read this.
Source: Kdnugget10662
Nothing takes the place of meaningful and substantive study, but these cheat sheets (that's really not a great term for them) are a handy reference in a pinch or for reinforcing particular ideas. All images link back to the cheat sheets in their original locations.
Source: Kdnugget25479
What better way to enjoy this spring weather than with some free machine learning and data science ebooks? Right? Right? Here is a quick collection of such books to start your fair weather study off on the right foot. The list begins with a base of statistics, moves on to machine learning foundations, progresses to a few bigger picture titles, has a quick look at an advanced topic or 2, and ends off with something that brings it all together. A mix of classic and contemporary titles, hopefully you find something new (to you) and of interest here.
Source: Kdnugget33912
In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.
Source: Kdnuuget19350
For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.
Source: Kdnugget10518
Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.
Source: Kdnugget14892
What do you think of this year's predictions? Do you see any new tools on the horizon, or do you believe data science popularity is due for a reckoning of sorts?
Source: Kdnugget7962
What do you think of this year's predictions? Do you see any new tools on the horizon, or do you believe data science popularity is due for a reckoning of sorts?
Source: Kdnugget3237
Strong math understanding, computing skills, critical thinking and presentations skills provide a strong foundation for a career in Data Science.
Source: Kdnugget5502
For the 2018 international women's day, we profile 18 inspiring women who lead the field in AI, Analytics, Big Data , Data science, and Machine Learning areas.
Source: Kdnugget13743
If we look back, there is a huge progress in the field of technology and innovations. Many technologies emerge as the future starts and today, some of them are on their way up in the industry. Technologies like Machine Learning, artificial Intelligence, Big Data Analytics, Cloud Computing, ChatBots, Wearable Devices, Drone, etc. The aim of discovering all these modern technologies is to improve the quality of life of the common people and make their daily job much easier than ever before. Many business and industrialist have heavily invested in this technologies and they are reaping rich rewards in the recent times. Out of all these technologies, Artificial Intelligence (AI) has raised its standard tremendously in the last 4-5 years.
Source: HOB6324
These are the most popular data mining books on Amazon. As you look to increase your knowledge, is there something listed here that is missing from your collection?
Source: Kdnugget10305
The best jobs right now in the planet include titles like data scientist, data engineer, and business analyst. Yet too often, employers find it hard to acquire the right person for a job. There is a persisting gap between the skills possessed by the current or the emerging workers and the abilities required by the business.
Source: HOB18582
Whether you are learning data science for the first time or refreshing your memory or catching up on latest trends, these free books will help you excel through self-study.
Source: Kdnugget25500
Looking to learn Python this year? Come check out a list of the best five courses to help start your journey and get you closer to being a Python pro!
Source: Dzone15369
We probably all know that obesity is becoming a big problem in the developed world and just becoming bigger. It's a mindset that more is always better like more food, more choice but that's not always the case.
Source: HOB12759
This post will point out 5 thing to know about machine learning, 5 things which you may not know, may not have been aware of, or may have once known and now forgotten.
Source: Kdnugget10707
The merging of Big Data and Cloud powered by Artificial Intelligence (AI) and Machine Learning will be the next game changers and we consider that it will be a multi-trillion dollar prospect for the Indian economy. We are in the halfway of the single largest renovation the world has even seen since the industrial revolution.
Source: HOB10332
The cabinet of Israel on Sunday approved a $264.5 mn nationwide program, which will span over five years to make health data about its population accessible to private companies and researchers.
Source: HOB10962
If you look at it closely, you will see that in some ways Martine Rothblatt, the chairwoman and founder of United Therapeutics and James Park, CEO and co-founder of Fitbit are infact unusual entrepreneurs and healthcare leaders. Both were outsiders to the industry and neither of them were well versed with the regulatory arts of American Healthcare.
Source: HOB5388
We take a look at five things you need to know about Big Data. There's a lot of social media and general internet buzz regarding Big Data, but what exactly is it?
Source: Kdnugget27669
Modi government has actively used big data and analytics in its governance to succeed and reform the country. Here is a look at some of the top use cases.
Source: HOB8490
There's been a lot of hype about Data Science... and probably just as much confusion about it.
Source: Kdnugget26022
IBM has showcased its new cloud offering called Cloud Private Data that has been designed to assist businesses make use of ML and data science techniques.
Source: HOB13746
We examined 140 frameworks and distributed programing packages and came up with a list of top 20 distributed computing packages useful for Data Science, based on a combination of Github, Stack Overflow, and Google results.
Source: Kdnugget12972
ARE ALGORITHMS taking over our jobs? Yes, yes they are... and that a good thing. With a focus on leveraging algorithms and balancing human and AI capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.
Source: Bizcatalyst3608400
Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.
Source: Hackerbits13371
Infosys has made an investment of $1.5 mn in Waterline data science, a provider of data discovery and data governance software.
Source: HOB5577
The merging of Big Data and Cloud powered by Artificial Intelligence (AI) and Machine Learning will be the next game changers and we consider that it will be a multi-trillion dollar prospect for the Indian economy. We are in the halfway of the single largest renovation the world has even seen since the industrial revolution.
Source: HOB19353
Data Science has become an integral part of making crucial business decisions in today's competitive market. This is one of the reasons companies are on a rampage to hire Data Scientists and qualified ones at that. The data science job interviews at companies like Facebook, Google, LinkedIn, AirBnB, Insight, Twitter, Mu Sigma have one thing in common - these interviews are tough. But we have a list of helpful data science interview questions from these companies that will help while someone is preparing to apply for the post of a Data Scientist.
Source: HOB76500
The cabinet of Israel on Sunday approved a $264.5 mn nationwide program, which will span over five years to make health data about its population accessible to private companies and researchers.
Source: HOB8871
If you are looking to learn python than what could be a better source than taking help from books written by professionals? In order to help you with your search we have created a list of best book for python data science, so that you don't have to wait and based on your requirements you can start your learning process with best books to learn python:
Source: Digital Vidya25383
We will see real-time big data come to the forefront in the enterprise world this year. There is a convergence of several factors that will lead to this. Companies have increasingly begun to use cloud solutions and advanced data processing solutions in order to derive business insights for improving customer experience, optimizing operational processes and providing executives with critical data points.
Source: HOB13389
Here are the four big data trends that will be driving change in the enterprise landscape in the coming year.
Source: HOB6777
I wrote this quick primer so you don't have to parse all the information out there and instead can learn the things you need to know to quickly get started.
Source: kDnugget13404
There are no cover articles praising the fails of the many data scientists that don't live up to the hype. Here we examine 3 typical mistakes and how to avoid them.
Source: Cyborgus9144
Personally, I haven't learnt as much from videos & online tutorials as much I've learnt from books. Until this very moment, my tiny wooden shelf has enough books to keep me busy this winter.
Source: AV14406
A comprehensive course on Hadoop for just $39.
Source: HOB7482
In this blog, we will discuss Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning. Also, will discuss each of these individually for better understanding.
Source: Data Flair34098
Based on historical data and accurately predicting some patterns out of it then, Data science comes into the picture. Data Science is the data driven decision making and building business strategies based on data analysis by removing any manual or judgemental error.
Source: HOB19152
Researchers are working to convert massive sets of big data into unique sound patterns in order to improve anomaly detection and comprehension.
Source: HOB7353
Natural language processing is a technology that combines big data and artificial intelligence together and brings it to the table.
Source: HOB38379
Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.
Source: Kdnugget11886
Coming European GDPR directive affects data science practice mainly in 3 areas: limits on data processing and consumer profiling, a "right to an explanation" for automated decision-making, and accountability for bias and discrimination in automated decisions.
Source: Kdnugget7872
The firm's top 10 prognostications on where technology will take us include shopping in AR, corporate fitness programs, and much more.
Source: PC Mag21585
I recently came across Rachel Tomas's article on the importance and value of writing about what you learn, and Julia Evans's advice on why and how to write, and thus I have decided to follow their advice and write an article (for the first time ever!).
Source: TDS24639
What Skills Do You Need To Go From Jr. To Sr. Developer. The role of a data scientist still varies from company to company and even team to team. This makes it much harder for companies to create a standardized growth plan for their data scientists.
Source: Hackernoon13158
This is a hard question to answer. Hang with me in this one (and this is not the final answer about the universe, existence and everything.
Source: TDS9405
I've had several conversations recently with people I know in the data science space that always start out about business and then drift to the state of data science as a whole.
Source: DataScience11988
Ability to write a production-level code is one of the sought-after skills for a data scientist role- either posted explicitly or not. For a software engineer turned data scientist this may not sound like a challenging task as they might have already perfected their skill at developing production level codes and deployed into production several times.
Source: TDS8820
Gartner predicts that the number of IoT devices will surpass 11.2 billion this year, the majority of which are in the consumer sector. The same report forecasts that the endpoint spending will exceed $2 trillion, in hardware and software combined.
Source: iotevolutionworld8430
A new report aims to help universities shape data analytics education. Technologies that collect data for analysis exist in pretty much every aspect of life - from smart thermometers in homes to wireless sensor networks.
Source: EdTech7356
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
Source: Kdnugget14088
With data now easier to collect and analyze than ever before, companies are recognizing the business value of such information - and the need to tap into it quickly. Enter the chief data officer (CDO), the newest role in the C-suite that is responsible for defining and putting into place a data management strategy.
Source: DataScience8025
I've read a number of articles stating how hard it was to get into Analytics and Data Science. This hasn't been my experience, so I wanted to share. We'll look at interviewing, the tools I currently use, what parts of industry I wasn't prepared for in school, and what my career trajectory has looked like. But not in that particular order.
Source: TDS9681
Tech improvement all across the industries have helped to develop Artificial Intelligence for advancement of businesses today. And, Machine Learning(ML) is a branch of AI. So, if we basically talk about Machine Learning (ML),it actually provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed. It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data. The process of ML is somehow similar to that of Data Mining. Both search through data to look for patterns. But, ML uses the data to improve the program's own understanding. ML programs are used to detect patterns in data and adjust program actions accordingly.
Source: HOB10770
Based on historical data and accurately predicting some patterns out of it then, Data science comes into the picture. Data Science is the data driven decision making and building business strategies based on data analysis by removing any manual or judgemental error. Data Science is everywhere and here is the rescue for how to be a data scientist.
Source: HOB6582
Data analytics is the key to drive optimal strategy for every business. It helps a business to effectively target consumers with marketing efforts while also creating products that facilitates in solving current problems.
Source: HOB6519
Natural language processing is a technology that combines big data and artificial intelligence together and brings it to the table. Today guests have choices like ordering delivery and meal kit services that threaten to disrupt margin-accretive dine in business. Because of this, a deep understanding of guest expectations is more important than ever before. Natural language processing helps you automate key guest insights to give you the upper hand.
Source: HOB5592
Are you currently pursuing your masters in Data Science? Overwhelmed with Buzzwords and Information? Don't know where and how to start your study? Then start with this article and a starter kit provided, but learn it for excellence and not just for the exams.
Source: Kdnugget11124
Top viewed videos on Big Data since 2015 include Big Data use cases in psychographics, sports, politics and data monetisation.
Source: KDnugget7233
Technology is making financial services, particularly lending, one of the most enthralling segments to keep an eye on. While several technological transformations going on, here below are two which are already in flourishing mode and ready to completely change the way financial sector services are offered online.
Source: HOB9414
In the world of postmodernism, Relativism has been, in its various guises, both one of the most popular and most reviled philosophical doctrines. According to Relativism, there is no universal and objective truth; rather each point of view has its own truth. You must be wondering why I am discussing it and how it is even related to Data Science.
Source: TDS17010
Data analytics is the key to driving optimal strategy for every business. It helps a business to effectively target consumers with marketing efforts while also creating products that facilitates in solving current problems. Businesses are using data analytics in various aspects within technological tools across departments, from IT management to customer support administration. By storing data safely and securely in a cloud-based backup system that uses file encryption, enterprises can protect private consumer information and business insights that provides them with a competitive edge.
Source: HOB4749
The merging of Big Data and Cloud powered by Artificial Intelligence (AI) and Machine Learning will be the next game changers and we consider that it will be a multi-trillion dollar prospect for the Indian economy. We are in the halfway of the single largest renovation the world has even seen since the industrial revolution. India, though persisted undiscovered in the early 90s, holds the potential to be revealed as creators of asset class because of their ability to create unstructured data sets. India is poised to leapfrog the world in Artificial Intelligence, as the demography is not controlled by the legacy usage of computers or tablets so they can transfer directly to the phone. Also, India is creating amorphous data at a very speedy pace and has data sets in bulk that very few countries possess.
Source: HOB8046
We probably all know that obesity is becoming a big problem in the developed world and just becoming bigger. It's a mindset that more is always better like more food, more choice but that's not always the case. This is a just similar phenomenon that we must choose the right food in the right amount to keep us healthy, same businesses must be judicious about what data they collect and what variety they have.
Source: HOB7311
Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Their effectiveness depends on the collective use by enterprises to obtain relevant results for strategic management and implementation.
Source: Maruti Tech Labs12222
For a myriad of data scientists, linear regression is the starting point of many statistical modeling and predictive analysis projects. The importance of fitting, both accurately and quickly, a linear model to a large data set cannot be overstated.
Source: TDS11169
Artificial Intelligence and Machine Learning technologies have made impressive strides in recent years, and thanks to platforms such as cloud, AI and machine learning capabilities are now widely available to organizations of all types and sizes. But as any seasoned technology leader knows, having technology on the shelf doesn't mean it will get accepted or used. It could simply just end up staying on the shelf.
Source: Forbes5937
Is data analytics fuelling the IoT revolution or is IoT fuelling data analytics revolution is a key question that businesses must look for. These two areas Data Analytics and IoT have a symbiotic relationship. One feeds on the other for growth and adoption. IoT connects all manner of endpoints, unraveling a treasure trove of data. The availability of this data is driving opportunity for data analytics by extracting and presenting useful information for decision makers to make informed decisions, which are more predictive in nature rather than reactive.
Source: HOB5250
Machine Learning Engineer, with avg. salary of $136K and Data Scientist, with avg. salary $133K are among the top US jobs in 2018, according to job site Indeed.
Source: HOB26034
For data science beginners, 3 elementary issues are given overview treatment: supervised vs. unsupervised learning, decision tree pruning, and training vs. testing datasets.
Source: HOB10293
Today, each of the many things we do everyday can literally be recorded. Every credit card transaction is digitalized and traceable; Our public presence is consistently being monitored by the many CCTV's hanging around every corner of the city
Source: HOB19194
Artificial intelligence (AI) is growing every day at a furious rate, and with it, the statistics surrounding the industry and the various industries it's revolutionizing are changing.
Source: HOB15345
As an organizer for a data science meetup group, I am often asked this question.
Source: HOB15345
Over the past year, I have had the opportunity to speak with a lot of prospective Data Science bootcamp students sharing my pre and post bootcamp experiences and helping them put in context some of the major factors they need to consider before deciding to attend a Data Science bootcamp.
Source: HOB7521
A person new to the Data Science field details their salary and the negotiation process.
Source: HOB10425
Taken from the answers experts gave, here is a compiled list of 5 essential actions and attitudes that keep data scientists learning after their degrees.
Source: HOB13959
A basic grounding in the principles and practices around artificial intelligence (AI), automation and cognitive systems is something which is likely to become increasingly valuable, regardless of your field of business, expertise or profession.
Source: HOB39777
Artificial Intelligence, Blockchain and Machine Learning globally are considered to be the holy trinity of technologies. Startups, SMEs and large corporations all together are looking at the various use case of these technologies. In a recent report, TMT predictions for 2018, Deloitte identified five key developments that will lead the popularity of machine learning in the future.
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If you are learning a new skill, think about HOW you are learning. My plan was to never be a student again. I like learning. I enjoy the process of learning and as a teacher and scientist, I am continuously engaged in this process.
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How do Machine Learning algorithms handle such large amount of data in companies (or real-life cases)? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
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I started working at www.comet.ml a few months ago, where we are building a really amazing tool for machine learning engineers. The short of it is that we help track experiments using a single line of code that automagically saves everything to make your model reproducible. You can get great experiment logging and history without being tied to a single platform.
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No matter what company does, in order to succeed in today's competitive environment, you need a robust infrastructure to both store and access the company's data. And, it needs to be done from the very beginning. And, thus the demand for skilled data engineers is rapidly growing and is projected to grow at a faster rate.
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Big data isn't just a buzz word anymore. It is extremely important for organizations to pay attention to it. Data is now driving more organizational decisions than ever before. With vast amounts of data being produced in real-time, there is a huge demand for people with skills to manage, analyze and help organizations use this data effectively. Technology changes frequently, and so do buzzwords. Big data, which was one of the most used terms until recently, has been replaced with 'real-time'. This doesn't mean that the demand for big data skills is now low. Rather, it simply means that the keyword has been replaced.
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I'm sure everyone who has been following tech industry news knows about "big data" and "AI." Although there is no industry-consistent definition for either term, most people tend to agree that both have been playing more and more important roles lately
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nyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals.
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I attended IBM's inaugural Think event in Las Vegas last week. This event, IBM's largest (estimated 30,000+ attendees!), focused on making your business smarter and included keynotes and sessions on such topics as artificial intelligence, data science, blockchain, quantum computing and cryptography.
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The practice of data science requires the use of analytics tools, technologies and languages to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals relied on Python, R and SQL more than other tools in 2017.
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According to Forbes, 53 % of organizations are embracing Big Data Analytics, which means the need to gather and secure data has never been this critical before.
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Marketing is in a transitional era and new technologies have changed so much about how marketers work from the responsibilities of the chief marketing officer, to what content is created, and why. Marketing teams have to improve their marketing strategies and customer outreach by tapping into data analytics and marketing automation. This includes the use of analytics to understand how current and potential customers behave and make decisions in different contexts and to analyze the design of demand generation programs to increase marketing performance and return on investment.
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Data scientists work at all levels of the organization. Our survey of data professionals revealed that Director-level data scientists have the highest level of proficiency across many data science skills and work with more of their peers compared to data scientists who are Individual Contributors, Managers or even Executives. Satisfaction with outcome of analytics projects did not differ across job levels.
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Big data is the major field in IT that is going to change many trends in the industry. Their individual applications are ginormous and will start a new era in the technological world. Most companies already understand the importance of using big data to drive insights and decisions and this is already on hype.
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As digital marketing strategies grow more sophisticated and complex, businesses find themselves wrestling with more data than ever.
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Today, big data has made it possible to reap immense benefits for businesses, right from sales, to marketing and accounting, and almost everything. Human Resources (HR) is not a business function that comes to our mind when we think about big data, but it is a business function that can acquire huge benefits.
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Teradata Corporation is a provider of database-related products and services. Putting all the infrastructure in place needed to drive real-time insight is a complex endeavor. The number of individual platforms that need to be stitched together is often daunting to even the most experienced IT organizations. Looking to make it simpler to achieve that goal, Teradata announced it has combined support for geospatial, temporal and time-series data into a Teradata Analytics Platform that can be queried using Python, R, SAS, or SQL-based tools.
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Data science requires the effective application of skills in a variety of machine learning areas and techniques. A recent survey by Kaggle, however, revealed that a limited number of data professionals possess competency in advanced machine learning skills. About half of data professionals said they were competent in supervised machine learning (49%) and logistic regression (53%).
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Student demand for degrees in the subject soars as employers seek skilled analysts
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This article covers why it's important to consider all the factors when being hired as a data scientist.
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It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
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Artificial intelligence is one of the highest demanding fields. It includes general AI, expert systems also known as data mining, machine learning, Neural Network and lastly, fuzzy systems. These have been essential and interesting topics among the students, scholars, faculties as well as professionals.
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Big Data have been heard for some time now. The concept of Big data is continually evolving and being reconsidered, as it remains the driving force behind many ongoing waves of digital transformation, including artificial intelligence (AI), data science and the Internet of Things (IoT). The data which is unstructured, time-sensitive or immense cannot be processed by relational database engines. So, this type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
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I recently made a Batman analogy when discussing the topic of data science with some colleagues. In this post, I will explore this analogy further.
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In the past few years, I have met up with a lot of employers and conducted interviews for training program. Through the conversations and interviews and seeing the end results, I thought I will share more on how to prepare for your resume and even the interviews for a data science role.
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Consumers choose brands that demonstrate their personal ethos. Customers get attracted to companies which are focused on real-time communication and which creates interesting content. To save your real customers, you must invest in digital marketing. Digital Marketing has played a vital role to portray the success of an organization.
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How do you combine historical Big Data with machine learning for real-time analytics? An approach is outlined with different software vendors, business use cases, and best practices. Big Data has gained a lot of momentum recently. Vast amounts of operational data are collected and stored in Handoop and other platforms on which historical analysis is conducted. Business intelligence tools and distributed statistical computing are used to find new patterns in this data and gain new insights and knowledge for a variety of use cases: promotions, up- and cross-sell campaigns, improved customer experience, or fraud detection.
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Using Reinforcement Learning to Tackle CitiBike Rebalancing Problems and Beyond
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According to an IDC Digital Universe Study, by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. Uncovering insights from this homogenous amount of information will require the seamless adoption of big data technologies, stronger data security, and integration of AI, machine learning and cognitive technologies applications with business operations
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No matter how large or small a company is, organizations across the globe generate a massive amount of data. While businesses have quickly come to realize that there is tremendous value to be extracted from the large volume of data that is collected, they are still not harnessing the opportunities that can be derived from analyzing data properly.
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Today, we live in a world which is highly driving towards digital transformation. Digital Transformation is more than just a buzzword - it's a process of using technology to radically change your business.
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Big data goes beyond volume, variety, and velocity alone. One should need to know these 10 characteristics and properties of big data to prepare for both the challenges and advantages of big data initiatives.
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Taking decisions based on Data is not only an inherent sense but a strong commercial sense too.
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A Machine Learning Framework is an interface, library or tool which allows developers to more easily and quickly build machine learning models, without getting into the nitty-gritty of the underlying algorithms. It provides a clear, concise way for defining machine learning models using a collection of pre-built, optimized components.
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The amount of data that companies collect and store today is staggering. However, it's not the volume of data being gathered that's most important it's what companies are doing with that data that matters most. With both unstructured and structured data streaming in from everywhere at an unprecedented rate, making connections and extracting insight is complicated work that can quickly spiral out of control.
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NEC Corporation (TSE: 6701) today announced the establishment of dotData, Inc., a new startup company based in Cupertino, California, in the heart of Silicon Valley, that develops and globally provides software that automates data science processes using artificial intelligence (AI).
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The amount of digital data in the universe is growing at an exponential rate, doubling every two years, and changing how we live in the world. Big Data is surely a big deal. We definitely are seeing an increase in activity with companies responding to the impact big data has made on their business.
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It seems like data/analytics has much less clarity around roles than most other common functions at tech companies. Do you think this will ever change? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
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As more and more Indian firms look to leverage big data for various aspects of their businesses, data science and analytics professionals are having their day in the sun.
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Artificial Intelligence is everywhere. The application of Artificial Intelligence is to improve the customer experience is on the rise. In fact, this year the Consumer Electronic Show featured its first ever Artificial Intelligence Marketplace to showcase the latest innovations designed to perform human tasks. Products ranged from big data analytics to speech recognition to advanced decision-making to predictive technology. Many of these solutions are already being leveraged by great companies to add a magic touch to their services.
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Every airline company wants the passengers to experience the flight like once in a lifetime experience. By always putting their passenger likes at the top and then as a result plans the whole thing. Also to bring a circumference over all of this, information is the key. They analyze the traveler s data, his preference and his fondness.
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Big Data can deliver remarkable marketing campaigns however, the success of the campaign will depend upon the enterprise's willingness to invest in technologies, processes, and its engagement with customers. Many consider marketing as an art nowadays, and it is, beyond doubt, a complicated piece of work, which requires craft as well as graft. Going back to the website's blunder, it is evident that there was a terrible misinterpretation in the analytical data they received. Today, direct marketing heavily relies upon Big Data analytics to pin down individuals based on their Google queries, browsing history, credit card swipes and so on.
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Ah the dreaded machine learning interview. You feel like you know everything... until you're tested on it! But it doesn't have to be this way.
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Data Science is getting very popular and many people are trying to jump into the bandwagon, and this is GREAT. But many assume that data science, machine learning, plug any other buzzword here, is to plug data to some Sckit-Learn libraries. Here is what the actual job is.
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I started my way in the Data Science world a few years back. I was a Software Engineer back then and I started to learn online first (before starting my Master's degree).
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This is a story about the danger of interpreting your machine learning model incorrectly, and the value of interpreting it correctly. If you have found the robust accuracy of ensemble tree models such as gradient boosting machines or random forests attractive, but also need to interpret them, then I hope you find this informative and helpful.
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Artificial intelligence is the future. Google, Microsoft, Amazon and Apple are all making big bets on AI. (Amazon owner Jeff Bezos also owns The Washington Post.) Congress has held hearings and even formed a bipartisan Artificial Intelligence Caucus. From health care to transportation to national security, AI has the potential to improve lives. But it comes with fears about economic disruption and a brewing "AI arms race ." Like any transformational change, it's complicated. Perhaps the biggest AI myth is that we can be confident about its future effects. Here are five others.
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Most people don't realize, but the actual "fancy" machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!
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There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning.
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There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are few amazing practical examples of AI and machine learning
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Big data is growing everyday and becoming a very popular word in the tech world. Many people around us keep talking about it, but do they know what it actually means? Big data is nothing but the collection of unstructured data. This data is not in a particular format and because of this its datasets sizes are generally huge measuring tens of terabytes and sometimes crossing the threshold of petabytes. The term big data was preceded by very large databases (VLDBs) which were managed using database management systems (DBMS). Having so much of data pertaining to the business provides a very niche way of increasing the sales or profits of any company. But in order to do so we need to make use of Big data analytics.
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Machine learning has been instrumental in solving some of the important business problems such as detecting email spam, focused product recommendation, accurate medical diagnosis etc. The adoption of machine learning (ML) has been accelerated with increasing processing power, availability of big data and advancements in statistical modeling.
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The exploding demand for data scientists is representative of a need that will not slow down anytime soon. The monetary value of storing, workings with and drawing penetration from data keeps falling the demand for professionals that can work with this data and deliver on the insight will continue to grow.
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Big data is now playing a large role in most industries, with the number of uses for information rising regularly as new algorithms and methods to analyze data are developed. The application of big data in taxation has eased the effort required for high levels of accuracy, and has increased the uses for tax information around the world. Big data in public administration has yet to play a significant role across the industry, however, its potential awaits untapped.
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In an organizational or business data analysis, you must Begin with the right question . Questions should be measurable, clear and concise. Design your questions to either qualify or disqualify potential resolution to your specific job or opportunity.
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Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data has been a crucial part of our lives.
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Data science is ubiquitous and is broadening its branches all over the world. The invisible hand of data science in the form of ranking algorithm governs the news streams and feed, recommendation engines that guide the content we see on Netflix and YouTube. Similarly, survival analyses for the estimation of time queues and neural networks for self-driving cars. But there includes a lot of challenges which hinders a data scientist while dealing with data.
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Becoming data scientist is really hard. Each & every project requires a different kind of programming language or software to focus on. There is an immense list of tools for data science. Here are top 8 data science tools in 2018:
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Big data can transform how decision-makers view business problems and inform strategic decisions, allowing them to rely upon objective data. Good data, sound analysis and valuable insights are critical for mitigating risks, making balanced strategic decisions and competing against others.
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In 2018, data and data analytics can't be ignored. Data Analytics is quite big buzz these days. Analytics is the combination of analysis and logics and it is the collection, measurement, analysis, visualization and interpretation of digital data illuminating user behaviour on websites, mobile sites and mobile applications. Following are the six ingredients of a successful digital analytics transformation.
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Today, organizations acroos the world are leveraging the power of big data in everything, right from designing workplace policies to social media recruitment. Big data has become a buzzword in business circles today. Big data can help a lot when it comes to talent acquisition.
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Artificial intelligence is the future. Google, Microsoft, Amazon and Apple are all making big bets on AI. Artificial Intelligence (AI) is all the rage from self-driving cars and Siri personal assistants, to chatbots and email scheduling agents that will take routine tasks out of human hands.
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More businesses and organizations are using data analysis tools, thanks to the explosive growth in the ground of data analytics has seen in recent years. With data analytics, and the kind of outputs expected from it, there are questions growing about the trust that is placed in it, and the fresh ways of decision making.
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With the growing amount of data available with the organization, Data analytics becomes an integral part of almost every business management. Analytics in itself has wide applicability to add more value to the business
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In the Big Data world, Web Scraping or Data Extraction services are the main and very first requisites for Big Data Analytics. Extracting data from the web has become very necessary for companies to survive in business.
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Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data has been a crucial part of our lives.
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Technology has been revolutionizing the way we perform tasks. It has made our tasks easier, simpler & faster. All thanks to tech revolution!! The technological innovations are the new big thing in 2018. Heard about AI, Machine Learning? Artificial Intelligence has always been in the favorites' section of tech experts and companies. Now, have a look on top 7 trending technologies in 2018:
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In 2018, data and data analytics can't be ignored. Data Analytics is quite big buzz these days. Analytics is the combination of analysis and logics. Analytics can't be performed without software's. By applying data analytics we can draw conclusion about the any given set of information.
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From Business to Government to Medicine big data is a technological buzz word now a day. Big Data is essentially the collection and analysis of large amount of data. In very broad terms it centers on the collection and analysis of large volumes of data.
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Analyzing the great potential of Big Data in health care industry, Doctors, Researchers, Scientist and other stakeholders are trying to real world evidence to influence their decision making. Healthcare industry is experiencing a rapid movement towards a new world where focus will be on value and outcomes.
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Analyzing the great potential of Big Data in health care industry, Doctors, Researchers, Scientist and other stakeholders are trying to real world evidence to influence their decision making. Healthcare industry is experiencing a rapid movement towards a new world where focus will be on value and outcomes.
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Manufacturing is important part of the world's economic development, but the roles it plays in advanced and developing economies has shaped dramatically. In developing countries, manufacturing operations bring new employment opportunities that are changing the societies. Manufacturing always remains important as a job initiator in the whole world.
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Predictive Analytics is a buzzword today. Wondering what is it? It's Applications? So, first of all, let's get started on the meaning of Predictive Analytics.
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Telecom sector is one of those industry which always looks for new trends and innovative technology to provide better services to its customer. Telecom Industry has already using the trending technologies such as AI, Chatbots, Machine Learning but the development in AI will be a turning point in terms of connectivity, communication, efficiency and delivering customer services.
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Recent advances in processing large amounts of data have generated a plethora of new opportunities to improve individual lives and the welfare of our societies. For example, smart meters monitoring domestic electricity consumption can help save electricity when not at home or suggest the cheapest electricity supplier depending on the consumption pattern.
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Growing amount of data available to the organization has led to the development of many analytical tools. As the big data has no significance to the company until and unless it can be converted into valuable insights.
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Big Data is the trendy term people use in describing the technology to solve the complex problems. The problems that seemed impossible to solve, now all can be easily solved thanks to the collection and analysis of a huge amount of data. It is clear that Big Data is here to stay in modern era and is also changing the world.
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We are in the midst of a gold rush in AI. But who will reap the economic benefits? The mass of startups who are all gold panning? The corporates who have massive gold mining operations? The technology giants who are supplying the picks and shovels? And which nations have the richest seams of gold?
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Banking Institutions and Financial Sector are on the urge of digital transformation. The increasing use of technologies lead to increased risk. With the digitization, the various sources of risks are bank's website, mobile trading applications, corporate banking platform and so on.
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Numbers don't lie, data analytics is on rise soon it will be integrate part of all the organizations. Data science has created so much hype in the world of IT sectors that from big to small companies all are now hiring employees who have knowledge regarding this subject. Data science helps an employee to understand data and then synthesize it in a proper way so that they can communicate in a better way which is beneficial for the companies
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Data is gold, The amount of digital data in the universe is growing at an exponential rate, doubling every two years, and changing how we live in the world. Big Data is surely a big deal. Data has replaced oil as the world's most valuable resource. Data can help us achieve tremendous things on both the levels ie individual and businesses, and as a society. But to extract the best value from data, you need to be using the best techniques and the best technology
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For beginners and students to have better understanding about the emerging technologies and charity about the artificial intelligence, one need to understand the basic terms, concepts related to it.
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When machines are connected to internet, we start getting real-time data in large amount. That real-time data is not only helpful in the Descriptive analysis but also helpful in understanding the behavioral patterns to get predictive and prescriptive analysis which means what will happen and actions we have to take. In short, with the emergence of IoT, the whole equation has transformed. Now we can leverage this data in our daily job to get best possible outcomes and also understand where their time and money are spent. So that we can use the resources in an effective manner hence wastage can be minimized.
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With the growing awareness about big data and data analytics, it has revolutionized many industries. Logistic is another field which is going to see transformations with the application of big data analytics in its process
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With the growing awareness about big data and data analytics, it has revolutionized many industries. Logistic is another field which is going to see transformations with the application of big data analytics in its process.
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Now a day's mobile became the central part of everyone's life. This shows marketer to exploit mobile generated data to deliver delightful experience to their customers. The connectivity through the mobile devices using different mobile apps can tell more about consumers' needs, expectations and behaviors.
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Country's second-largest telecom service provider Vodafone is leveraging Artificial Intelligence (AI) and Big Data technologies to enhance consumer experience. Technologies such as Artificial Intelligence and Big Data are helping to understand customer preferences better and that enable to cater to them accordingly.
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Check out this collection of 9 (plus some additional freebies) must-have skills for becoming a data scientist.
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From data driven strategies to decision making, the true worth of Big Data has been realized, and has led to opening up of amazing career choices. Check out these 12 interesting careers to explore in Big Data.
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The shift towards data driven approach has created demand for data analyst. The application of big data and data analytics is increasing day by day. So, career in data analytics will provide students with opportunity in different industries.
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"Hey Siri, what is IIoT?" Every time you ask Siri, you get an accurate answer. Wonder how your virtual assistant suggests, thinks and talks like a human being? Because it has been taught that way through machine learning.
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E-Commerce industry has been growing constantly. The latest technological advancement have certainly accelerated its growth and on its way. The e-commerce industry has been expressively influenced by the rise in mobile internet usage, IoT and big data. Big data can be used for efficient trailing of transactions, sales analysis and for forecast demand and supply
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E-Commerce industry has been growing constantly. The latest technological advancement have certainly accelerated its growth and on its way. The e-commerce industry has been expressively influenced by the rise in mobile internet usage, IoT and big data. Big data can be used for efficient trailing of transactions, sales analysis and for forecast demand and supply
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Artificial intelligence (AI) and machine learning are transforming the global economy, and companies that are quick to adopt these technologies will take $1.2 trillion from those who don't.
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Google unveiled an AI that can make reservations over the phone. Has the Turing Test been finally passed?
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Big Data is on hype and due to the growing demand; good data is becoming a valuable commodity. Big data is the act of collecting large data sets from traditional and digital sources to identify trends and patterns. That collected information is used by the companies to improve what they know about customer's wants and needs.
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In the digital era, every industry is achieving transformational change. Digitization in each sector is reducing cost of operation and improving the efficiency. Similarly, wealth management is facing with digital disruption that can transform the traditional wealth management.
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Increased user base on digital channels, growing awareness of big data and implementation of data analytics are elevating marketing efforts and its impact on bottom-line. Now, smart marketer use predictive analytics to their grow business.
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Python, Scala, R and Java are the major languages for big data, data mining and data science. They all have their own fame. These languages are easy to use and understand obviously they have their own pros as corns as wel
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Python, Scala, R and Java are the major languages for big data, data mining and data science. They all have their own fame. These languages are easy to use and understand obviously they have their own pros as corns as wel
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In this feature, we take a look inside the working lives of people whose job titles often warrant the question: 'but what do you actually do?' This week, we speak to Glenn Bunker, data scientist at realestate.com.au.
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Emerging technologies are changing the face of retail operations and commerce. Digital world is continuously posing threat on retail industry as the customers are getting engaged with their mobile phones and expect more convenience.
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R is a great choice for manipulating, cleaning, summarizing, producing probability statistics, and so on. In addition, it's not going away anytime soon, it is platform independent, so what you create will run almost anywhere, and it has awesome help resources.
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Google have recently introduced ML Kit, a machine-learning module fully integrated in its Firebase mobile development platform that is available for both iOS and Android.
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Data science informs entrepreneurs in a way that listening to their gut just can't.
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Scientists are paid the big bucks for a reason, and that's because a lot of people don't understand what the heck it is we do. But that's why writing is important. It helps us to understand what we do, and explain it to others so they can understand it, too.
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Machine Learning (ML) has become massively popular over the last several years. And why... well simply because it works! The latest research has achieved record breaking results, even surpassing human performance on some tasks.
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Machine learning can potentially redefine not only how education is delivered, but also nurture quality learning on the student's part. Probably the most important part of the role of machine learning in teaching is customized teaching. With machine learning, we are moving away from the one-size-fits-all methodology.
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Cynthia Saunders-Cheatham is a senior careers director at Johnson at Cornell. She says more employers want MBAs with data analytics skills
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Artificial intelligence and machine learning is no more hype. These technologies are changing our life, the way communicate, the way we do business and the way we live. We can say many examples where AI and machine learning has transformed our daily life.
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Data Science Congress 2018, an initiative by Aegis School of Data Science and mUni Campus concluded with a bigger bang this year with 2000+ delegates and 100 speakers across industries. DSC 2018 was inaugurated by Shri Vijay Goel, Minister of State for Parliamentary Affairs and Statistics and Programme Implementation, Govt. of India.
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It also brings out the fact that India currently contributes about 10% of the open job openings for data scientists globally, making it the largest data science hub in the world outside the U.S.
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Data scientists have become the darlings of today's competitive job market. Entry-level salaries can range into six figures, and roughly 700,000 job openings are projected by 2020. There's good reason for this spike in demand, too.
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We probably all know that obesity is becoming a big problem in the developed world and just becoming bigger similiarly data analysis is becoming an important part of the businesses growth. Big data is the act of collecting large data sets from traditional and digital sources to identify trends and patterns. It is very necessary to get understand the structured and unstructured data in order to make right decision for the businesses to grow.
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Convolution neural networks (CNNs) are a family of deep networks that can exploit the spatial structure of data (e.g. images) to learn about the data, so that the algorithm can output something useful.
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Customer survey is the primary source of deriving customer feedback for many companies, despite the growth in embracing of other customer feedback sources like social media, call center conversations and emails. It usually contains structured questions, requesting customers to rate their level of satisfaction with their experience. Two common customer surveys are relationship and transactional surveys. The main difference between these two surveys is that relationship surveys measure attitudes about the experience and transactional surveys measure attitudes in the experience. These approaches of the survey are used to help businesses to improve the both strategic and tactical decision-making power.
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Harvard hosts some of the most prestigious programs in the world, especially in business and law. So it was big news in the data science industry when the university announced a new master's program in data science in March 2017
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Now big data and analytics lies in the heart of every organization but data will act as a garbage for data scientist until and unless they are not able to visualize the data and its impact on key activities and the business outcome.
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In retail business there is some form of typical competition that has been evolved from an longer period in business .The retail business is to identify the needs of the customers in which the best sales can shine out among the competitors . The response of every business folks is to analyze and make comparison to other businesses in your industry. Find if there is something useful to the customers as well as to your business and the good response is to define your brand and consistently communicate your own Unique Selling Proposition. This blog will gives you the 5 strategic ways through which you were able to beat the competition and also remain in business strong and lucrative.
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Real-time data and analytics are now entering in the field of HR as well. Big data sets are the collections of both sets of structured and unstructured information. However, the command of big data doesn't lies in its size, but lies in that how organizations make use of the resource. Big data comes from many sources and delivers organizations with deep and meaningful insights into consumer and client characteristics as well as internal enterprise performance.
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Technology revolution all over the world are changing the business landscape and making the world more competitive. The data is growing exponentially and the only way to survive is to exploit data intelligence and analytics.
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Not that long ago, the concept of "Big Data" was pretty abstract. Few companies considered it feasible to sift through huge sets of data looking for speculative insights.
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The amount of data being collected globally is increasing exponentially. This proves that we are currently in the age of Big Data. Simultaneously, many more related fields like Data Science, Data Intelligence, Artificial Intelligence, Machine Learning, and Deep Learning have also seen a lot of growth and are revolutionizing businesses and industries across the world and making the competitive world. To remain the part of this competitive market organizations lookout for the skilled data scientists and data professionals.
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Cloud computing permits new level of agility for developers, data scientists and IT by providing a pay as you go model with unlimited scalability and no hardware cost. Analytics is one of the most important business functions that basically fit for public and hybrid clouds. Companies are turning to cloud based analytics for easier access to increasing amounts of data, greater data sharing and collaboration, faster insights and time to value and to reduce operational cost.
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Indian Navy is planning to incorporate big data analytics and artificial intelligence into their operational functioning of the forces. In the conference of the commanders they reviewed the Navy's "Mission Based Deployments". The purpose of the review is at maximizing benefits ensured from the deployment of Indian Navy ships and aircraft to the dangerous areas within the Indian Ocean Region.
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The UK technology sector will have to lose its fear of failure and embrace the artificial intelligence revolution, the Minister for Digital, Culture, Media and Sport has argued.
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While Bitcoin and Cryptocurrency may have been the first widely known uses of Blockchain technology today, it's far from the only one. In fact, blockchain is revolutionizing every industry. Blockchain is the biggest technological breakthrough since the internet and becomes the most darling topic in the tech sense. There are many blockchain applications but here we talk about that how blockchain is impacting on Big Data.
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Based on historical data and accurately predicting some patterns out of it then, Data science comes into the picture. Data Science is the data driven decision making and building business strategies based on data analysis by removing any manual or judgmental error. Data Science is everywhere and here is the rescue for how to be a data scientist. Data scientists basically deal with huge no of both the data whether structured or not structured and the future of data scientists will be data psychology.
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Establishing trust in data is an essential requirement for businesses and entities for whom credible, reliable information is the lifeblood. As enterprises seek to manage data as an asset, it becomes increasingly vital that data sources are trusted and verifiable.
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Inditex, the world's largest clothing retailerand owner of Zara stocks up on Artificial Intelligence, Big Data and analytics into its business strategy to stay ahead in the race of competition. The biggest fashion retailer is hooking up with tech companies and hiring talent from startups and partnering Analytics and investments, which offers an AI-powered consumer behavior forecast platform.
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The IoT is changing our lives right from how we drive, to how we make purchases and even how we get energy for our home, and what not? So, as you sensed much hype about IoT, let's have a look on what actually is IoT and how does it work?
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TransUnion, the Chicago-based credit reporting agency, has funded the creation of a new professor position at the University of Illinois at Chicago in hopes of addressing a growing need for experts in the rapidly expanding field of data science.
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Big data is used to identify patterns and trends that can yield powerful insights into human interactions majorly consumer behavior. The data includes demographic, geographic and psychographic attributes collected from diverse sources throughout the consumer cycle as well as from the other field of the individual's life.
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This post examines the evolution of data processing in data lakes, with a particular focus on the concepts, architecture and technology criteria behind them.
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Unlike other projects in the organization, AI data-driven products are new for most of the organizations and the best way to go from research to production. Data science has created so much hype in the world of IT sectors that from big to small companies all are now hiring employees who have knowledge regarding this subject. The data science industry's job market is hot today
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Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases.
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From detecting screen cancer to sorting cucumbers to detecting escalators in need of repair, machine learning has granted computer systems entirely new abilities. But how does it really work under the hood? Let's walk through a basic example and use it as an excuse to talk about the process of getting answers from your data using machine learning. Here we learn the art, science and tools of the machine learning.
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Artificial Intelligence is the broader umbrella under which Machine Learning and Deep Learning come. Deep Learning and Machine Learning are the subset of each other. It's all about tremendous increase in data so results can't be predicted accurate, hence AI comes into the picture and now it is talk of the town.
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Data science has created so much hype in the world of IT sectors that from big to small companies all are now hiring employees who have knowledge regarding this subject. Data science is helpful for the employees to get understand about data and then make it in a proper way so that it can be communicated in a better way which is valuable for the companies.
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By now, we all know Machine Learning models makes predictions by learning from the past data available. So here we have input value, a machine learning model based on those inputs understands and gives out the predicted output.
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Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data science and data analytics, people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. However, although they may sound similar, the terms are often quite different and have differing implications for business. Knowing how to use the terms correctly can have a large impact on how a business is run, especially as the amount of available data grows and becomes a greater part of our everyday lives.
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In 2018, data and data analytics can't be ignored. Data Analytics is quite big buzz these days. Analytics is the combination of analysis and logics. Analytics can't be performed without software's. By applying data analytics we can draw conclusion about the any given set of information.
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Machine Learning has become increasingly important today because of the digital transformation of companies leading to the production of massive data of different forms and types, at an ever increasing rate. Due to the advancements in computing technologies and exposure to huge amounts of data, the applicability of machine learning is dramatically increasing.
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Enough of talking about languages, we should focus more on the technologies so that we can build apps and can solve different problems. In the fast-changing technological era, one needs to keep themselves to be updated with the technology that is emerging or having great influence in our life. Being updated with trends, market conditions and technology helps in cope up with the changes and move in a right direction
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Bots built upon machine learning need long training processes to have the ability to hold a meaningful conversation with real people. Training data become, therefore, a diamond in the rough; all companies need such input for their bots. Until now, this data was generated in a slow manual way. However, speeding up your bot training can now come true with artificially generated data.
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Web scraping is also known as web harvesting or data extraction. It is used for extracting data from the website. Web scraping a web page included fetching and extracting. Fetching is something what we are downloading. So we can say that web crawling is a most important component of web scraping, to fetch pages for later processing. Once fetched, then the extraction takes place. It is also used for contact scrapping, and as a component of applications used for web indexing, web mining, and data mining.
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A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.
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Stagraph is a new simple visual interface for R, which focuses on data import, data wrangling and data visualization.
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In a perfect world, data scientists would take subjectivity out of their conclusions when examining findings.
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Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. And it provides historical, current and predictive views of business operations.
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Leveraging the application of big data, whether it is to improve the process of product development, improve customer retention or work through the data to find new business possibilities organizations are more relying on the expertise of data scientists to sustain, grow and beat their competition. Consequently, as the market for data scientists increases, the system presents an exciting career path for students and existing professional.
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For all those who are searching for job, year 2018 is going to be the year where you can move further in the tech industry and here is the list of top 10 highest paying jobs.
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In our last post, we started using Data Science for Credit Risk Modeling by analyzing loan data from Lending Club.
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No,but if you have some knowledge about data mining algorithms. That will be beneficial in learning data analytics. Data Analytics is a field demanding a variety of skills. Having knowledge of Hadoop is one of them.
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Today, we're going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons!
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In order for data scientists to be effective at a startup, they need to be able to build services that other teams can use, or that products can use directly.
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Analytic is the interpretation of the data collected. Raw form of data is not of any use. It is important to convert raw data into analytic so that it is easier to find that what is the decision that are to be taken by the use of analytic. Businesses use this analytic for making actionable business analytic that will help them in taking real time actions.
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Industries are now leveraging the chatbots in their daily operating functions thus all the activities are performed by bots with minimum efforts and they can be more focused towards another important task in the organisation. The bots gain the popularity and now they are talk of the town.
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Machine Learning is changing the way we do things, and it has started becoming main-stream very quickly. While many factors have contributed to this increase in machine learning, one reason is that it is becoming easier for developers to apply it. And, that is through open source frameworks. Yet most would agree that these days the largest fraction of machine learning researchers come from computer science.
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Data Analysis and data analytics are often treated as interchangeable terms, but they hold slightly different meanings. Data analysis is the overarching Data Analyst practice that encompasses the use of data analytics tools and techniques to achieve business objectives
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Interactivity allows you to embed much more information than in a static visualisation by using tooltips, click-events, ability to filter etc.
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AI for Diagnostics, Drug Development, Treatment Personalisation, and Gene Editing
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Big Data
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Aviation Industry is on radar next and it bets on Big Data Analytics. This industry needs to move beyond its present way of working so big data analytics is only key to unlock the potential.
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DeepMind and other universities has published many End to End Reinforcement Learning papers that are used for problems that can be solved by a single agent. End to End RL algorithms learns both feature representation and decision making in the network by taking pixels as the input and the controls as output.
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Apache Spark is an open-source cluster computing framework for real-time processing. It has a thriving open-source community and is the most active Apache project at the moment. Spark provides an interface for programming entire clusters with implicit data parallelism and fault-tolerance.
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We can't expect a simple black or white answer to this question. Whether data or algorithms are more important has been debated at length by experts (and non-experts) in the last few years and the TLDR, is that it depends on many details and nuances that take some time to understand.
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Let me start by saying that there is no one way to being a self taught data scientist. You have got to find out what works for you.
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For any tech enthusiast, knowing certain Machine Learning Algorithms and its applications have now become very important. Tech giants like Google, Amazon, Facebook, Walmart are using Machine Learning significantly to keep their business tight enough to compete with their rivalries.
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Doctors have always written clinical notes about their patientsâ??-â??originally, the notes were on paper and were locked away in a cabinet. Fortunately for data scientists, doctors now enter their notes in an electronic medical record.
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In finance, data are (very) noisy, and often non-stationary. 'Signals' cannot be split from 'noise' in any unique way, as a matter of principle. This is very different from, say, image processing, where the level of noise can be controlled, at least in principle.
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The growth of data is not going to stop. This will usher in new challenges and opportunities. Below you can find the five hottest big data trends for techies and business.
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Machine Learning is overrated in a few ways, both by people with little experience and, more perniciously, people deeply invested in the field. Machine Learning is overrated in a few ways, both by people with little experience and, more perniciously, people deeply invested in the field.
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Data Science is on the rise, both from a company's perspective and from an employee's perspective. This makes data science a great field to get into at the moment.
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Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
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Python and R are the two most popular programming languages used extensively by Data analysts and Data Scientists. Both the languages are free and open-source and were developed in the 1900s. However, following are some of the reasons which gives Python edge over R that makes it even more popular among the community.
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The retail industry traditionally has been generating detailed data on consumer behaviour and purchase history through various transactional and customer relationship systems. Exploiting these data to increase basket value and optimize margin remains a challenge with traditional technologies of business intelligence tools and data warehouse architectures.
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Essentially, blockchain is a distributed ledger, it's a shared database. Rather than records existing in one location, they are shared across computers in the network all over the world. Built into that ledger is a consensus mechanism that allows anybody to interact or do business with each other and to trust each other without having to go through a central intermediary.
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Google Cloud announcements bring deep learning and big data analytics beyond data scientists, but enterprises will want more.
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As we know organizations from different domains are investing in big data analytics nowadays by analyzing large data sets to uncover all the hidden patterns unknown correlations, market trends, customer preferences and other useful business information. These analytical findings are helping organizations in more effective marketing, new revenue opportunities and better customer service and they're trying to get competitive advantages over rival organizations and other business benefits. An Apache Spark and Hadoop are the two of most prominent big data frameworks and people often comparing these two technologies.
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AI has changed the ways that how all business performs and now artificial intelligence is also changing the way the construction industry does the business. The construction industry has found undeniably a great partner in technology. After a long period of time, now the technology can boost productivity, safety and other critical aspects of business success.
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The analytics play a vital role in every kind of business because it takes every organization to the next level by reducing major stress in business. There are so many analytics tools has been introduced in the market to analyze data rapidly where this leads to tackle so many trolls in business without relying on others. The data Analytics lets you track business such as your store and generates reports that will help you with current updates on your business.
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Data Scientists are big data wranglers. They consider mass and messy data both structured and unstructured and use their formidable skills and organize them accordingly. They use or apply their analytics power to uncover hidden solutions to business challenges.
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Machine Learning plays an integral role in many areas of financial services like from approving the loans, managing assests, minimizing the risk and many more. Machine Learning plays a vital role in fraud detection and protects and thus protect the consumer from the fraudlent activities.
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A collection of the most exemplary examples of data visualizations, including Napoleons invasion of Russia and the iconic London Underground map.
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The skills that have remained important are (a) understanding the fundamentals of statistics, optimization, and building quantitative models and (b) understanding how models and data analysis actually apply to products and businesses.
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Some algorithms are better at learning with small data while others are preferable for large data. This fact can be understood rigorously through statistical learning theory. Intuitively, algorithm that chooses from a large or complex collection of models needs a larger data set to converge to a model that generalizes well to new data. Thus there is a trade-off between how complex model one wants to be able to learn and how much data and therefore also compute resources that one can provide.
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The challenge of pricing has entered a new era and every industry should learn from what's happening in fashion right now. While competition with Amazon often gets a lot of attention, industry upheaval due to the rise of e-commerce is much broader than that. Retailers need to embrace external market data, in real time, in order to succeed.
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Big data and its implications are impacting every business from one-person companies to Fortune 500 enterprises. As data collection, analytics and the interpretation of that data become more readily accessible, they will have an impact on every business in several important ways, regardless of what field you operate in or the size of your business.
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Big data and its implications are impacting every business from one-person companies to Fortune 500 enterprises. As data collection, analytics and the interpretation of that data become more readily accessible, they will have an impact on every business in several important ways, regardless of what field you operate in or the size of your business.
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The big fashion brand H&M is betting on the technology Artificial Intelligence and Big Data to regain profitability. All the big brands are now coping with these technologies and maximizing their profit for the business so that they can stay longer in the market.
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The big fashion brand H&M is betting on the technology Artificial Intelligence and Big Data to regain profitability. All the big brands are now coping with these technologies and maximizing their profit for the business so that they can stay longer in the market.
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The amount of data that e-commerce companies collect is changing what we can to do for customers. Increasingly, commercials and ads are targeted to our specific demographics.
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Every company requires different sets of talents, while some jobs require theoretical knowledge; some jobs are based more on your practical knowledge. Same is the case when it comes to getting jobs in data science, getting jobs in data science has it owns unnerving effects. A great portfolio in this case is a good way to showcase your skills what you are good at what your strengths are. This is the best way to show your employers your skills that you have been learning.
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The ever-improving capabilities of big data platforms increasingly create new opportunities for industries with representatives who want to examine analytics to benefit their companies.
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AI is not the new technology it is very broad concept and comprises a set of powerful technologies that are emerging under it like deep learning, Reinforcement Learning and Facial Recognition and many more. AI is trending these days and yes it is the future.
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Data Scientists is the highest paying and demanding job in the current year. All HR professionals are under pressure to get hired the best data science talent so they can achieve the valuable business insights. According to Harvard Business Review described the role of data scientists as it is the sexiest job of the 21st century.
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Blockchain as a buzzword has been already picking up the speed in digital transformation. JD.com China's largest retailer recently announced it is launching a new blockchain technology platform for use by enterprise customers to build, host and use their own blockchain applications. Blockchain has the potential to change the way we interact with each other and with centralized third parties forever, simply because we do not need them with this system.
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Computers are always been quicker than humans at consuming, calculating and computing data and artificial intelligence have benefited a boon for our global economy. Combining insights from computer science with cognitive science can advance human thought, shape outcomes in ways that transform the process of human capital development and enhance overall employee contributions and engagement.
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Here we get to know that how companies can use systems of insights platform to improve the data sourcing, analysis and insights and how they are managing their data. Recently TIBCO has also published a webinar on know the answer of this question. According to director of analytic strategy, Shawn Rogers that how closed-loop SOI platform offer a continuous learning solutions. All the experts are always in search of getting good or valuable data so that they can get best from that by using their analytical skills.
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The most important thing you must know if you want to get succeed as a Machine Learning engineer is how you should deal with the most precious thing called "DATA". Data analysis is the most important thing that you need to master in order to proceed with Machine learning. Although it may sound surprising, unless you are able to analyze the data correctly, you cannot build a model to use on the data. Now Data analysis is a pretty big field in itself and to work on data analysis.
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The travel industry has changed a lot thanks to the internet. Earlier we used to go to brick-and-mortar travel agents to book a holiday tickets but now the whole scenario is being changed that mostly we book our flights and accommodation online. Internet is changing our lives and now a day's most of the work and personal lives has been digitalized.
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The travel industry has changed a lot thanks to the internet. Earlier we used to go to brick-and-mortar travel agents to book a holiday tickets but now the whole scenario is being changed and we book our flights and accommodation online. Internet is changing our lives and now a day's most of the work and personal lives has been digitalized.
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From a very generic perspective it does not matter how small or big the company is, it depends on the amount of data the type of data and how are they using the data. As per the recent trend, the need to handle with large amount of data is increased. As per the business requirements we are not able to achieve 100% accuracy when we are dealing with normal data conversions.
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Language doesn't mean that this is on 5th position is bad or language is at 1st position is the best language. It's just the classification and giving them the number's.
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Everyone is talking about artificial intelligence and it is also changing our lives. Business world hasn't fully jumped on board yet with artificial intelligence but very soon most of the organizations are thinking to deploy it and gain its benefits.
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Much of the AI capability working its way into BI tools today isn't entirely new consumer technologies have been successfully implementing them for years. In fact, this is one of the strengths of artificial intelligence when applied to business intelligence the fact that users already understand intuitively how to use products like Google and Amazon making it easier for them to adopt those same technologies and interaction paradigms in BI tools
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Solving a complete machine learning problem for the societal benefit
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In data science, computer science and statistics converge. As data scientists, we use statistical principles to write code such that we can effectively explore the problem at hand.
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With traditional TV viewing on the decline, we discuss several ways Big Data and Machine Learning can assist with online video, including redefining user recommendations, improving video buffering and leveraging MAM orchestration.
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Gain some perspective on the Netflix interview process, and on ways to prepare for just such an industry interview.
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Sometimes you open a big Dataset with Python's Pandas, try to get a few metrics, and the whole thing just freezes horribly.
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I would like to live in a world whose systems are build on rigorous, reliable, verifiable knowledge, and not on alchemy. Simple experiments and simple theorems are the building blocks that help understand complicated larger phenomena.
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Interviewing is a skill. Here are 10 tips and resources to improve your Data Science interviews.
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Check out part 2 of this excellent series of articles on becoming a data scientist, written by someone who spends their day recruiting data scientists. This installation focuses on learning.
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We examine what's important for data scientists in their careers, including challenging work, networking with peers, foreseeing their career path and creating a good work-life balance.
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When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? We hope not!
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When I heard about the work at UNDP Global Pulse, I thought it was something straight out of a movie. Data science for social impact sounds like the coolesting thing I can do with mathematics.
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The deep learning market is expected to reach the heights by the year ending 2023. Deep Learning is experiencing a rapid increase in its application across various industries. The marketing industry is one of those sectors that are leveraging deep learning for improvements.
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It has been a while even since I posted on Medium. Having been in Data Science for almost half a year, I've made a lot of mistakes and learned from the mistakes along the wayâ?¦ through the hard way.
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We asked our LinkedIn group members what their greatest challenges were to becoming fully fledged data scientists. Some of the most common frustrations were:
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As someone who has been there, I'd like to outline a few practical ideas to help junior data scientists get started at a small software company. The steps were drawn from my personal journey and that of others before me.
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Data Science, Machine Learning, Artificial Intelligence and data analytics are quite buzzword. No doubt these technologies will transform all the industries but most likely the technology that will change our profession in the short run will be Robotic Process Automation (RPA).
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Data Science is quite a large and diverse field. As a result, it is really difficult to be a jack of all trades. Traditionally, Data Science would focus on mathematics, computer science and domain expertise.
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The growth and success of the organization totally depend on your ability to collect good amount of data, managing those data which leads into business insights and outcomes. Data is all around only matter is that we have to collect good structured data which is further helpful for the organizations.
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Deep Learning is one of the hottest technologies out there. There are many research papers in Deep Learning, and it can be really overwhelming to keep up.
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R is an open source language. R is one of the most popular programming languages used for statistical analysis and graphics. It provides the variety of statistical and graphical techniques, and it is highly extensible. This language is used by statisticians and data miners for developing statistical software and data analysis.
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How do you think data science will change over the next 10 years? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
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Useful tips to get started writing about your data science projects
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Machine learning is having a huge impact in almost all the industries. From Google brain to self-driving cars all are made using a machine learning algorithm. It is currently growing and expanding rapidly and more and more people are learning it.
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I was listening to an old episode of Partially Derivative, a podcast on data science and the news. One of the hosts mentioned that we're now living in the "golden age of data science instruction" and learning materials. I couldn't agree more with this statement. Each month, most publishers seem to have another book on the subject and people are writing exciting blog posts about what they're learning and doing.
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This curated list of mindset-changing books will help you become a better Data Scientist
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A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
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I hope Part 1 sold you on the idea that version control is a critical tool for managing data science experiments. But the devil is in the details, so let's talk about how to implement version control in a data science project.
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What You Need to Know about Machine Learning. This eBook offers you the perfect place to lay the foundation for your work in the world of Machine Learning, providing the basic understanding, knowledge, and skills that you can build on with experience and time.
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Nowadays, there is a huge list of powerful data visualization tools to help you illustrate your ideas, visualize your data, make it talk, share your significant analytics with customers and the global community.
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I recently had the chance to use machine learning to address an issue that is at the forefront of the American media, the difficulty of recognizing fake news.
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Harvard Business Review has regarded data scientist as the sexiest job of the 21stcentury. In this article, with the assistance of Octoparse V7, one of the best free web data scraping tool, we aggregated the resources and tools that you may need to become a data scientist.
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Machine Learning opportunities can be sparse, so when you finally get invited to that long-awaited Machine Learning Interview, you want it to go perfectly. Let me show you how.
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The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.
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Actionable takeaways from a memorable experience As we moved into August and summer begins to wind down, I thought I'd take the time to reflect on my last 12 weeks as a Data Science Intern for Unity Technologies in San Francisco, CA.
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At the moment of writing this post about Data Analytics and Data Science, I am bootstrapping a data literacy consultancy, catering to large enterprises around the globe.
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Did ATMs wipe out the bank tellers? No. Did PDFs wipe out the print industry? No. Did self-service checkout wipe out cashiers? Still no. Still, many jobs are changing thanks to the onslaught of automation and AI, resulting in types of new roles and responsibilities.
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Machine learning is an application of artificial intelligence that gives a system an ability to automatically learn and improve from experiences without being explicitly programmed. In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular).
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Big Data Analytics is the perfect solution for the complex problems. Every marketer have to take right decision for their organization and now for all those decisions they need not to be depend on their employees. With the help of big data analytics they can easily come to know about each fact and findings.
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Completing your first project is a major milestone on the road to becoming a data scientist. It's also an intimidating process. The first step is to find an appropriate, interesting data set. You should decide how large and how messy a data set you want to work with; while cleaning data is an integral part of data science, you may want to start with a clean data set for your first project so that you can focus on the analysis rather than on cleaning the data.
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Here details of the top 9 data science skills that potential data scientists must have to be competitive in this growing marketplace from the perspective of a recruiter.
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It's often said that data is the new world currency, and the web is the exchange bureau through which it's traded. As consumers, we're positively swimming in data; it's everywhere from labels on food packaging design to World Health Organisation reports.
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We have covered all the courses at a beginner level. As i have classified these courses on the basis of difficulty level. Here we have come up with 200 online programming language and computer science courses at an intermediate level.
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Preparing for an interview is not easy - naturally, there is a large amount of uncertainty regarding the data science interview questions you will be asked. No matter how much work experience or technical skill you have, an interviewer can throw you off with a set of questions that you didn't expect.
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I came across a challenge on Kaggle called PASSNYC: Data Science for Good. Nelson Mandela was right. Education is a powerful weapon as well as one of life's greatest gifts.
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Data Science is very hot and demanding field which contains methods and techniques from the other fields like statistics, machine learning, artificial intelligence, Bayesian and many more other fields. The main purpose of these fields is to generate meaningful insights from the collected data.
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A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.
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Crunchbase lists over 5,000 startups who are relying on machine learning for their main and ancillary applications, products and services today.
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Modern data science emerged in tech, from optimizing Google search rankings and LinkedIn recommendations to influencing the headlines Buzzfeed editors run. But it's poised to transform all sectors, from retail, telecommunications, and agriculture to health, trucking, and the penal system.
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Despite the struggles, it is possible to leverage data science and machine learning to scale your business, save time and grow revenue while improving your customers' experience.
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Six months ago I quit my job as a junior JavaScript developer and traveled around south-east Asia for five months. Within a week of getting back to the UK, I had three job offers and had accepted an offer for almost double my previous salary. It wasn't easy, but it was worth it. Here's how I did it.
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As data science and big data get the hype in the industry, handling it is a challenge we need to deal with. Along with all the pros on one side keeping yourself advent with the latest improvements in this field is another issue which bothers us.
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The popularity of data science, big data jobs in India and globally has leapfrogged over the past five years or so. Its successful run in the industry can be attributed to better research, project implementations and the general growth in big data and data science. These developments have called out for techies trying to make a career in data science.
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I want to explore the real challenges of data science, based on perspectives from those in the field and those who manage them. However, no career is without its challenges, and data science is not an exception.
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This article list data sets from the data science world that you might find interesting.
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We covered 50 data sets for data scientists that are amusing in part 1. In part two we cover 50 more of those.
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50 data sets that data scientist find amusing.
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Sentiment Analysis examines the problem of studying texts, like posts and reviews, uploaded by users on microblogging platforms, forums, and electronic businesses, regarding the opinions they have about a product, service, event, person or idea. Sentiment Analysis has become a hot-trend topic of scientific and market research in the field of Natural Language Processing (NLP) and Machine Learning.
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Raise your hand if you've been caught in the confusion of differentiating artificial intelligence (AI) vs machine learning (ML) vs deep learning (DL)...
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Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science.
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Machine Learning (ML) models are making their way into real-world applications. We all hear news about ML systems for credit scoring, health-care, crime prediction.
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What do data science, data analytics, and business intelligence mean at Grab and how are they being used? - Wong Mun
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Whether you are learning data science for the first time or refreshing your memory or catching up on the latest trends in Data Mining, Data Analysis, these free books will help you excel through self-study.
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This "classic" (but very topical and certainly relevant Big Data) post discusses issues that Big Data can face when it forgets, or ignores applied statistics. As great of a discussion today as it was 2 years ago.
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The Tech Companies are hiring for various positions in India and abroad for Data Science, Artificial Intelligence, Machine Learning. Know the eligibility criteria, job location, and profile. Find out what all is needed to work with the biggest tech-giant in the world
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More free resources and online books by leading authors about data mining, data science, machine learning, predictive analytics, and statistics.
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Demand for data scientists is at an all-time high and will continue to be at record levels in the coming years while these professionals become more scarce.
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Deep learning, artificial intelligence, and neural networks are challenging new concepts for many, but an intensive short course from ICHEC aims to plug the knowledge gap.
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I would never use as a data scientist, or too shallow: not giving me enough information to help me understand how to be effective with Docker quickly.
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Are you a hard-core enthusiast of data visualization? Or a beginner, who wants to learn and be able to create more effective visualizations? Check out our list of 35 invaluable books you must read for better visualization.
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There is considerable hype around data science. Websites and social media are flooded with articles on Big Data, Data Science, and Data Analytics. These fields are projected as top fields, while data scientists are considered as saviors of the world and hence are supposed to be highest paid professionals.
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Data analytics is becoming more vital for businesses, and data scientists are in high demand. But the emerging field is broad, and some companies say they have struggled to find job candidates whose skills fit their needs.
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The world around us is rapidly changing with machines becoming more intelligent. Machines learn from data that we have collected over the years and what we generate each day. Machine learning is not a new concept but was actually coined by Arthur Samuel in the year 1959
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IBM recently predicted that in the next two years there might be a boost of 28 percent in the number of employed Data scientists.
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Social Media data is the new gold and analytics for the businesses. Social media analytics is an amazing art and science of extracting valuable hidden business insights from the platform called social media.
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Embarking on a journey through the lands of machine learning? Here are few important lessons like Feature Engineering, Model tuning, Overfitting, Ensembling etc. which you should keep in mind along the way.
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With data sciences being the focal point, Entrepreneur India analyses why this new trend is now the essential tool for a majority of smart entrepreneurial solutions.
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Most developers have dubbed Python as the Swiss Army Knife in the data science community. It is easy to understand the reason behind it.
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Machine learning has tremendous potential to transform companies, but in practice it's mostly far more mundane than robot drivers and chefs. Think of it simply as a branch of statistics, designed for a world of big data.
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Here is a non-exhausting list of curious problems that could greatly benefit from data analysis.
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Learn about ten machine learning algorithms that Everyone should know to become a data scientist. Machine learning practitioners have different personalities. While some of them are "I am an expert in X and X can train on any type of data," where X = some algorithm, others are "right tool for the right job" people.
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The average company faces many challenges in getting started with machine learning, including a shortage of data scientists.
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A collection of Big Data trends to familiarize yourself with, covering IoT Networks, Artificial Intelligence, Predictive Analytics, Dark Data and more.
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In today's world, whatever your job, having skills and knowledge in Data Science will play a huge role in your career development. For example, big data and analytics gathered from customers allow marketers to build more effective digital marketing campaigns.
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Understanding data is key to unlocking job opportunities - Harvard Gazette. New course hopes to give students an edge in the job market.
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Data Scientist is the very demanding role in every big organisation and they have power to unlock the hidden values in the data's. They are very good at data handling and manages data at a very huge scale.
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This list of data analyst interview questions is based on the responsibilities handled by data analysts. However, the questions in a data analytic job interview may vary based on the nature of work expected by an organization.
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Data Scientists are expected to have a broader set of skills, which is realistically not possible. We believe a time will come when we expect more specialisation and collaboration by data scientists, rather than expecting one person to know everything.
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Until a few years ago the work of data scientists was isolated and mattered primarily for research and/or R&D purposes. The industry has been extremely thankful for the contributions of these clever individuals but we need them now in the mainstream.
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Researchers have looked at ways to utilize Blockchain for improving Artificial Intelligence. Blockchain developers make a good case on why the distributed ledger system is the perfect platform for testing the next generation of developments in AI
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Today every person is talking about the new business ideas and wants to start thier own dream company. But in Data Science this is little bit different and the success in this filed is mainly driven by knowledge on the particular subject. So for this practice every data scientists should read books and gain the insights.
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The 50 companies on the just-released LinkedIn Top Startups list in the U.S. have a whopping 3,069 open jobs, a reflection not only of their ambitions and expansion plans but also an ever-tightening national labor market, with more positions going unfilled.
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There are several ways companies can alleviate the pain and accelerate the data science transition, particularly as it relates to the IT department.
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The advertising world faces the problems of fraud and there is also lack of control over the privacy of data which are the most important thing for every business. So with the help of Blockchain technology these problems can be solved easily.
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Ability to write a production-level code is one of the sought-after skills for a data scientist role- either posted explicitly or not. For a software engineer turned data scientist this may not sound like a challenging task as they might have already perfected their skill at developing production level codes and deployed into production several times.
Source: HOB3342
Ability to write a production-level code is one of the sought-after skills for a data scientist role- either posted explicitly or not. For a software engineer turned data scientist this may not sound like a challenging task as they might have already perfected their skill at developing production level codes and deployed into production several times.
Source: HOB6840
Artificial Intelligence allows many applications and services which we use on daily basis. As technological revolutions has become a norm in this era of innovation and Artificial Intelligence plays very crucial role in recent advancements. By using smart applications of artificial intelligence we can save real-time existing process and permitting data-driven decision making on a faster timeline. No one can deny its importance as big companies like Google, Facebook, Amazon and Microsoft are investing in AI technology.
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What are the most to least important skills, and the types of people who apply for data science jobs. I talked about all of the skills I want in an ideal candidate. But since that candidate doesn't exist, I have to prioritize what attributes the candidates that I hire have.
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The artificial intelligence (AI) industry has been leading the headlines consistently, and for good reason. It has already transformed industries across the globe, and companies are racing to understand how to integrate this emerging technology.
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Machine learning and artificial intelligence have multiple applications in various fields. Communications and marketing is no exception. Just like other industries, it can help improve how smoothly everything runs and offer additional insights that were difficult to obtain manually.
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Data scientists are responsible for unlocking these insights and extracting intelligence which influences our lives, in commerce and at home.
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I dropped out of a top computer science program to teach myself data science using online resources like Udacity, edX, and Coursera. The decision was not difficult.
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The big data analytics technology is a combination of several techniques and processing methods. What makes them effective is their collective use by enterprises to obtain relevant results for strategic management and implementation.
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Data Science is an exciting field to work in, combining advanced statistical and quantitative skills with real-world programming ability. There are many potential programming languages that the aspiring data scientist might consider specializing in.
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People often ask me How can I learn Data Analytics? and I often stumble upon this question How to become a Data Analyst on Quora too. The answer is pretty much clearly available all over the internet. The actual issue is not how to become a data analyst but it is if we are ready to become one?
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I was asked recently on twitter a question that I've been asked in one form or another several times since I became a Senior Data Visualization Engineer at Netflix.
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Machine Learning is the term which we have been Hearing Now everywhere. When I Started My ML Journey Back in April 2016 I was like complete Noob having No Idea what ML is I used to Think does ML means that Machine are learning ?
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All marketers are familiar with the value of monitoring and analyzing web data and analytics. Most of the organizations are using Google Analytics to know the facts and figures of the organization. All extracted data leads to the efficiency, revenue growth and success of a business.
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Python continues to take leading positions in solving data science, Machine Learning, Deep Learning, Data Scraping tasks and challenges. Last year we made a blog post overviewing the Python's libraries that proved to be the most helpful at that moment.
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Appearing smart should be the top priority of every Data Scientist. This is what drives your career, right? Sarah Cooper's advice on how to appear smart in meetings is a fantastic start, but to succeed among Data Scientists is a whole other ballpark.
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Data Science has been hailed as the transformative trend that is set to re-wire the industries and re-invent the ways people do things. Products and applications are being developed in agriculture, healthcare, urban planning, trade, commerce, finance, and the possibilities are growing.
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Why Data Analytics? To answer this question, you need first of all few clues about my background. I am Rachid Oukhai, founder of Upsilon, Peculium (Data).
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In today's world with the introduction of "Internet of Things" and the improvement of "AI Technology" has led to implementation of big data solutions to almost every organization whether small or large organizations. With the help of big data analytics the management can easily take the decision and improve their operational efficiency.
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It isn't easy to find a web developer who hasn't contemplated the idea of switching to machine learning. And they can't be blamed, why deal with monotonous CSS declarations and JavaScript programs when you can challenge yourself with the most interesting problems of artificial intelligence?
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Machine Learning algorithms enable software applications to predict more accurately outcomes. Most of the organizations use top machine learning platforms to build the models that can receive data from the various sources.
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Who's on top in usage, interest, and popularity? Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around.
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Around Data Science Platforms all of its functionalities like data exploration and integration from several sources, coding, model building are executed. Data science platforms are programmed to train and test models and deploy the results to solve real-life business challenges.
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In the ever-changing ecosystem of data science tools, you often find yourself needing to learn a new language in order to keep up with.
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Technological revolutions have become a norm in this era of innovation. Artificial Intelligence offers organizations a competitive advantage. There is also considerable pressure on organizations to go the AI route for fear of losing an edge to competitors.
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Everyone wants to develop "skills in Machine Learning and AI" but few are willing to put in the hard yards to develop the foundational understanding of the relevant Math and CS
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Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don't quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting
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Sometimes you open a big Dataset with Python's Pandas, try to get a few metrics, and the whole thing just freezes horribly. If you work on Big Data, you know if you're using Pandas, you can be waiting for up to a whole minute for a simple average of a Series, and let's not even get into calling apply.
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Data visualization is helping companies worldwide to identify patterns, predict outcomes, and improve business returns. Visualization is an important aspect of data analysis. Simply put, data visualization conveys outcomes of tabular or spatial data in a visual format.
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Survey of company leaders shows the vast majority are planning to hire data scientists as economic uncertainty continues.
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Big data is used by companies around the world to inform and improve countless business processes, from customer service to marketing campaigns. But the ability to collect and analyze vast amounts of information isn't just useful for external operations; it can help you strengthen your business internally, too.
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Understanding how artificial intelligence (AI) and machine learning (ML) can benefit your business may seem like a daunting task. But there is a myriad of applications for these technologies that you can implement to make your life easier.
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In general explanation, data science is nothing more than using advanced statistical and machine learning techniques to solve various problems using data and Data Analysis. Yet, it's easier to just dive into applying some fancy machine learning algorithms -and Voila! You got the predictionâ??-â??without first understanding the data.
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There are a lot of computer science graduates and programmers applying for programming, coding, and software development roles at startups like Uber and Netflix; big organizations like Amazon, Microsoft, and Google; and service-based companies like Infosys or Luxsoft, but many of them have no idea of what kind of programming interview questions to expect when you're applying for a job with these companies.
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Assembling all the machine learning pieces needed to solve a problem can be a daunting task. In this series of articles, we are walking through implementing a machine learning workflow using a real-world dataset to see how the individual techniques come together.
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Data science is the top skill to learn in 2019, SlashData said. It noted that 45 percent of developers want to gain expertise in data science and machine learning, with other most-wanted skills including UI design (33 percent) and cloud-native development (25 percent).
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Much of this data is trapped in free-text documents in unstructured form. This data is needed in order to make healthcare decisions.
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Now a day's most of the organizations are using Data Science capabilities to shape the next set of products which will be more personalized and dynamic. Businesses across verticals have been sitting on huge quantities of data over the years, in various forms like customer, partner, and internal data.
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If you like a trendy career, you have that opportunity right now and get hired by the big industries. According to the Harvard Business Review, Data Scientists - "The Sexiest Job of the 21st Century". This article talks about the Data Science Courses, Certification, Tutorial and Training for Data Scientists
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Many companies want to use Data Science to advance their businesses. They recognize the need of data science as every organization prime goal is to stay competitive and make use of their data, but many of them are unsure of how to get started and don't even have a data scientist team.
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Many companies want to use Data Science to advance their businesses. They recognize the need of data science as every organization prime goal is to stay competitive and make use of their data, but many of them are unsure of how to get started and don't even have a data scientist team.
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There are numerous of Big Data tools for data analysis today. Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making.
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Artificial intelligence is really invaluable right across the spectrum of space problems.
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Everyday the amount of data we produce is very huge in number, but this pace is only accelerating with the growth of the internet of things (IoT). Here is the collection of some favourite stats which create huge amount of data every single day.
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Experts have made serious arguments about the use of Big Data in smaller businesses. What these smaller businesses need is a solid plan/strategy according to which they can set up big data in their smaller projects initially.
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Experts have made serious arguments about the use of Big Data in smaller businesses. What these smaller businesses need is a solid plan/strategy according to which they can set up big data in their smaller projects initially.
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There's a lot of conversation lately about all the possibilities of machines learning and deep learning to do things humans currently do in our factories, warehouses, offices and homes. While the technology is evolving-quickly-along with fears and excitement, terms such as artificial intelligence, machine learning and deep learning may leave you perplexed.
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In Part-2 collection of 10 Big Data Analysis Tools As Data Visualization and Sentiment Analysis Tools are mentioned. There are numerous of Big Data tools for data analysis today. Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making.
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Scholarship recipients will learn the Machine Learning, Deep Learning framework PyTorch for Artificial Intelligence Research.
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The large information that keeps on increasing or the data sets that are complicated need to be processed which is not possible by the traditional applications.
Source: Zion Market Research5043
Companies are aware with the importance of digital data in businesses. All organizations had already started to understand and identify what valuable data they can possess. So they are collecting it, analyzing it and using it to improve their business and operations. They are leveraging machine learning applications that help them in solving complex hurdles.
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There are lot of online free resources available from where we can get insights and use that knowledge in our daily routine. But here is the collection of good Big Data books that matches in-depth and comprehensive detail for the readers and these books are selected on following parameters like relevancy, popularity, ratings, publish date and ability to add value to businesses.
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In Part-2 we have covered 10 Big Data Analysis Tools as Data Visualization and Sentiment Analysis Tools. There are numerous of Big Data tools for data analysis today but here we cover big data analysis tools as data extraction and databases tools.
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Big data analytics is pouring off real-time sources like sensors and devices. Much of this data requires immediate analysis, for valuable insights while the information is still relevant.
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Machine Learning is the very popular technology and its demand is increasing day by day. Now all the organisations are looking for Machine Learning engineers and this is the highest paid profile in the industry. Here we take you through all the aspects of machine learning from simple linear regressions to the latest neural networks, and you will learn not only how to use them but also how to build them from scratch.
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I have split this post into four sections: Machine Learning, Natural Language Process, Python, and Math. I have included a sampling of topics within each section, but given the vastness of the material, I can't possibly include every possible topic.
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Machine Learning is the subset of Artificial Intelligence which allow systems to perform the specific tasks. Machine Learning and Data Mining work closely as both search through data to look for patterns basically it detects patterns in data and adjust program actions accordingly.
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Algorithms and Data Structure are language agnostic and any programmer worth their salt should be able to convert them to their language of choice. Unfortunately, I have come across several programmers who are REALLY good on programming language e.g. Java, knows minor details of API and language intricacies but has very poor knowledge of algorithms.
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Who has had prior stints in retail and education industry talks about the difference between Data Science, Machine Learning, and Big Data - Abhinav Rai, Data Scientist at an upgrade.
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Machine Learning and Artificial Intelligence are the two most trending technology everyone want to get learn and get started their career in these field. This field is not only in demand but also the highest paid profile. Here if you are at beginner level to learn machine learning and artificial intelligence follow these videos as you can gain more insights to start your career.
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Despite my math background, I've always considered myself first and foremost a consumer researcher who uses data analytics to gain insights into human behavior, and that's what I focus on at Farmers Insurance. Today, if you're a small business owner, you need to get that data just as much as any large corporation. And even if you've always been math-averse, you can learn how to apply analytics to your own business.
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In the Internet of Things, objects have their own IP address, meaning that sensors connected to the web can send data to the cloud on just about anything: how much traffic is rolling through a stoplight, how much water you're using, or how full a trash dumpster is.
Source: HOB3978
In Part-1 we have covered how to get started with Machine Learning at beginner level. Here in Part-2 we learn machine learning at two other levels. Machine Learning and Artificial Intelligence are the two most trending technology everyone want to get learn and get started their career in these field. This field is not only in demand but also the highest paid profile.
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There has been a rise of big data which has had impacts on social media taking it to all new levels. According to a survey by the year 2020 the collective volume of big data might reach close to 44 trillion gigabytes.
Source: HOB7935
If you are interested in pursuing a career in Machine Learning and don't know where to start, here's your go-to guide for the best programming languages and skills to learn, interview questions, salaries, and more.
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Youtube is a very popular source to educate and entertain people. To learn Artificial Intelligence, Machine Learning and Data Science youtube proved as a good online source by the users. This is great source for educational videos. Now all these technologies are becoming popular day by day and everyone want to learn and get start their career in these fields so they are searching these video channels on youtube.
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Machine learning (ML) continues to amaze us with its abilities and is set to transform the economic structure of many industries -- from producers of widgets to financial analysts and health care providers.
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Data is Gold, The amount of data in the universe is growing at an exponential rate and it is the most important valuable resource not only for individual but for businesses as well. But to get the best value from data you need to be using the best techniques and the best technology.
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These two fields Data Science and Artificial Intelligence are gaining popularity and are in demand and everyone can't afford to spend too much money on books so here is the collection of online free e-books on Machine Learning starting from basics of statistics, proceeding to machine learning foundations and finally advanced level.
Source: HOB3840
These two fields Data Science and Artificial Intelligence are gaining popularity and are in demand and everyone can't afford to spend too much money on books so here is the collection of online free e-books on Machine Learning starting from basics of statistics, proceeding to machine learning foundations and finally advanced level.
Source: HOB37848
Artificial Intelligence is very big term which includes a wide range of other technologies under it. One of those is a natural language processing (NLP), which act a mediator and also create intelligent communication between the man and machines. This can be possible with the help of code, computer linguistics, and computer science to help understand and manipulate human language.
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Python is very popular because of its set of robust libraries that make it such a dynamic and a fast programming language. Python is the fastest-growing programming language in the world, as it increasingly becomes used in a wide range of developer job roles and data science positions across industries.
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You can use Quick Code to discover more free programming language courses based on different technology and programming languages. Take these courses to learn programming, web development, front-end development, mobile application development, data science and start learning.
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Data Scientists is gaining much attention in the age of analytics at the same point of time the role as a Data Engineers are equally important. Data Scientist filed is evolving and they plays a dual role in the organizations. Data engineers are responsible for the databases, data pipelines, and data services that are prerequisites to data analysis and data science.
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In 2018 it's good time enroll for Data Analytics and it can't be ignored as analytics is the combination of analysis and logics. Data informs a large amount of decisions with strong groundwork rather than just taking shots in the dark.
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In 2020, the world is expected to generate 50 times more data than in 2018 and Data Scientist has been voted as the "Sexiest Job" by Harvard Business Review, there is a significant growth in demand for data-savvy professionals in businesses, public enterprises, and several nonprofit organizations.
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Recently I decided to get more serious about my machine learning and data science skills. So I decided to practice my skills, which led me to Kaggle.
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Machine learning is enabling investors to tap huge data sets such as social media postings in ways that no mere human could. Yet, despite the enormous potential, its record remains mixed.
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I analyzed the Stack Overflow survey and found a stark contrast in the salaries of software engineers, youth, interest in new tools, opinions about AI, ethics and more...
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Best big data courses listing displays the 'Best Course,' 'Product Description,' 'Rating,' 'Students Enrolled' as well as 'Product's Image' and 'Enroll Now' Button to purchase the Courses from the website for your convenience.
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All data technologies are evolving and getting a lot of hype in the industries. Now most of the organizations are leveraging these technologies ie. Artificial Intelligence, Machine Learning and Data Science. In this article there is a colloection of few artificial Intelligence, Machine Learning and Data Science start-ups in India.
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A data scientists has to spend long hours of work in the preparation of the project that phase takes more time than they actually work the one that is shown in newspaper on TV or on the internet.
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Artificial Intelligence is changing the way we live. These technologies are on hype as from our phones to devices like Amazon Alexa the artificial intelligence is robot is all around. Google, Netflix, data companies, video games and more all use AI to comb through large amounts of data.
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Big data is giving the necessary edge to the organisations; analyzing customer's behaviour, personalizing the customer's experience which directly improves the customers experience and their companies sell improving the company's revenue.
Source: HOB8625
While Bitcoin and Cryptocurrency may have been the first widely known uses of Blockchain technology today, The blockchain is gaining popularity only because of its security feature. This is the safe and secure ledger which permits all users to record transactions on system so it will be safe, secure and incorruptible. For this they code in the following best programming languages.
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The University's Data Science Institute recently incorporated the new Center for Data Ethics and Justice - founded by the University's Bioethics Chair Jarrett Zigon - in an effort to ramp up its focus on ethics in analysis and interpretation of data. This partnership has created a new course for graduate data science students that specifically addresses ethical issues related to the handling of data and advancement in technology.
Source: HOB6750
Data is not for only Analytics team now as Data Scientists are in demand and data covers all most many roles in an organization and employees need the literacy to handle it effectively. Now in most of the organization the data skills matters a lot because data is gold for all the industries and it is in very huge numbers as well.
Source: HOB4599
So, you have developed some interest in Python programming language and you have even planned to learn it.
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Predictive Analytics is a buzzword in today's technical world. Data is in huge quantity and it is very hard to manage those data so predictive analytics come into the picture. Wondering what is it? So, firstly we should start with the meaning of Predictive Analytics that what exactly it is.
Source: HOB4101
Predictive Analytics is a buzzword in today's technical world. Data is in huge quantity and it is very hard to manage those data so predictive analytics come into the picture. Wondering what is it? So, firstly we should start with the meaning of Predictive Analytics that what exactly it is.
Source: HOB2379
Predictive Analytics is a buzzword in today's technical world. Data is in huge quantity and it is very hard to manage those data so predictive analytics come into the picture. Wondering what is it? So, firstly we should start with the meaning of Predictive Analytics that what exactly it is.
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Data Science is a very huge field and it allows fresher and experienced professionals to kick-start their career in this evolving field. Now HR of the companies are seeking for Data Scientists CV and ready to pay the highest salary. For this most of the people are struggling to get learn about data science.
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A collection of useful mobile applications that will help enhance your Popular data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more.
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Data & Data Science is not just for the analytics team anymore. While data scientists are still in demand, the newest conundrum facing today's organizations concerns the rest of the staff. Data isn't used in a vacuum: it touches many other roles, and those employees need the literacy to handle it effectively.
Source: HOB31083
According to the August 2018 report by the employment-oriented service, data science skills are rising in demand across industries in the U.S.
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Mastering the little things in Python, NumPy, and Pandas for Data Science. here's the stuff that I'm always forgetting when working with Python, NumPy, and Pandas.
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My Mario Kart reflexes aren't what they used to be, but I am better at data science than I was as a fourth grader, so in this post, I'll use data to finally answer the question "Who is the best character in Mario Kart?"
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As part of my personal journey to gain a better understanding of Neural Network in Python, I've decided to build a Neural Network from scratch without a deep learning library like TensorFlow. I believe that understanding the inner workings of a Neural Network is important to any aspiring Data Scientist.
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Are you looking for Free online Data Science courses? Yes, it is possible. Data science plays a very crucial role when it comes to companies growth. Why? because with the help of Data Scientist, a company can know more about the behavior of its users or potential clients (For example - Amazon and Flipkart).
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If you're thinking of learning Python-or if you recently started learning about Python-you may be asking yourself- What exactly can I use Python for?
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For the past year, we have compared nearly 8,800 open source Machine Learning projects to pick Top 30 (0.3% chance).
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A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master's program using online resources.
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Data is very important and all the organizations are running towards data to collect it and store it safely. In today's world "Data Is Gold" and data has the potential to turn businesses into revenue. It can be possible only when if we use data is a perfect manner. To analyze and process data models, machine learning is very important. It involves large dynamic datasets to train itself, test and perform predictive and prescriptive analysis.
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Java is one of those technologies that have maintained itself over time, it has never gone outdated, and it has been a trustworthy platform. Java has a role which is everywhere even with so many new technologies coming and being around.
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Machine learning is undoubtedly on the rise, slowly climbing into 'buzzword' territory. This is in large part due to misuse and a simple misunderstanding of the topics that come with the term.
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Technology Business Research (TBR) recently come out with a report and its headline is, "Winning The Business Of Digital Transformation Services Requires A Process-Led Approach" authored by Sebastian Lagana and Jennifer Hamel. The report is full of good nuggets, but what all professionals especially liked the way that they categorized the 3 phases of Digital Transformation in his report:
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Maths is a major part of entering the field of data science but there's a lot more to it than that. When it comes to data science, there is a world of possibilities for those who want to enter the industry.
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There is no more field left by Data Science as with the help of Data Analytics the medical science will move to the new level from computerizing medical records to drug discovery and genetic disease exploration. Data Science and medicine are continuously developing and it is important that they both will advance together.
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There is no more field left by Data Science as with the help of Data Analytics the medical science will move to the new level from computerizing medical records to drug discovery and genetic disease exploration. Data Science and medicine are continuously developing and it is important that they both will advance together.
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Now every second person wants to start his/her career in Machine Learning, Data Science as all are the highest paid fields. Machine Learning interview questions are the subpart of data science interview and this is the one way to gear up your career as data scientist, machine learning engineer or data engineer and many more.
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Now every second person want to start his/her career in Machine Learning, Data Science as all are the highest paid fields. Machine Learning interview questions are the subpart of data science interview and this is the one way to gear up your career as data scientist, machine learning engineer or data engineer and many more.
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With the demand for data scientists exceeding supply by 50 percent to 60 percent, there is a pressing need for more talent in organizations. And you can use that your advantage to establish a wonderful career. So, yes, snagging a data science job is certainly an excellent idea.
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Many teams try to start an applied Artificial Intelligence project by diving into algorithms and data before figuring out desired outputs and objectives. Unfortunately, that's like raising a puppy in a New York City apartment for a few years, then being surprised that it can't herd sheep for you.
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Working with technology industry, it's impossible to run away from the vast impact of Artificial Intelligence and Big Data analytics. At the same time it is quite difficult to figure out where one technology begins and the other takes over. While it is clear there is a connection between the two, understanding the manner in which Artificial Intelligence and Big Data work together to solve business and operational problems is a key part of using the technologies effectively.
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Clearly, a job in Internet of Things can pay well because of the rising demand, but candidates will require a combination of skills to ensure a promising IoT career.
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In 2018, every organization has a data strategy. But what makes a great one? It's harder to tell the difference between a modest success and excellence. Indeed, in data science
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Artificial Intelligence, Data Science, and Machine Learning all are very popular technologies in this technological world. All companies are leveraging these technologies and getting best out of it and Tensor Flow, Google's Machine Learning API, which are used to develop the Rank Brain algorithm for Google Search.
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Data is everywhere. It is the most important resource in today's world and every organization is busy in collecting the huge amount of data. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012.
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Data is everywhere. It is the most important resource in today's world and every organisation is busy in collecting the huge amount of data. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012.
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Data is everywhere. It is the most important resource in today's world and every organisation is busy in collecting the huge amount of data. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012.
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Top 100 Data Science Resources & Tools For 2018 Whether you're just getting started in data science, or are gearing up to get a Masters in Data Science or Business Analytics, there's always more to learn. This guide gathers 2018's top data science resources on the web for learners of all stages.
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This list contains free learning resources for data science, machine learning and big data related concepts, techniques, and applications. Inspired by Free Programming Books.
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Artificial Intelligence and its pros and cons have been discussed to no end. Artificial Intelligence isn't a new-age discovery. In India, the Centre for Artificial Intelligence and Robotics (a DRDO Organization) was established as early as 1986. So the question to ask is why all the fuss now?
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The popularity of the blockchain has exploded recently by the fashion effect of ICO's, but all are not worth the time and money to invest in. Let's be aware of the power of this technology and all that one could build of use with it before the crypto trading kills it.
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The popularity of the blockchain has exploded recently by the fashion effect of ICO's, but all are not worth the time and money to invest in. Let's be aware of the power of this technology and all that one could build of use with it before the crypto trading kills it.
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Harvard Business Review has regarded data scientist as the sexiest job of the 21st century. One of the best free web data scraping tool, we aggregated the resources and tools that you may need to become a data scientist.
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If you're looking for a change of pace, learning to code in Python might the way forward. Coding offers different opportunities, good pay, and possibly the chance to work from anywhere on your own schedule. Coding is also one those future-proof jobs; until the machines take over anyway.
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Deep learning is one of the hottest topics of this industry today. Deep Learning is evolving and it is top of the Data Science world. Deep Learning has led to amazing innovations, incredible breakthroughs, and we are only just getting started. A lot of people carry an impression that deep learning involves a lot of mathematics and statistical knowledge.
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Difference Between Big Data and Machine Learning- Data drives the modern organizations of the world so don't be surprised if I call this world a data-driven world.
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Deirdre Dempsey has decades of experience dealing with data analytics, data science stemming from a strong foundation in mathematics.
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If you want to become more data driven than you should have good understanding of all the languages and you're supposed to be master in Data Science field as well. Building the first data project is actually not that hard only you should know the basic steps and categorize them from raw data to building a machine learning model.
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Machine learning offers organizations the potential to make more accurate data-driven decisions and to solve problems. Now organizations are leveraging the machine learning technology and this is not the magic it presents many of the same challenges as other analytics methods.
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People when talk about Artificial Intelligence, Machine Learning, Automation, Big Data, Cognitive Computing, or Deep Learning they're talking about the ability of machines to learn to fulfill objectives based on data and reasoning. This is tremendously important and is already changing business in practically every industry.
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Dark data is a type of unstructured, untagged and untapped data that is found in data repositories and has not been analyzed or processed. Gartner defines dark data as the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).
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Python is one of the most popular programming languages of recent years. Its clear syntax and readability make it the perfect coding language for beginners. It's forgivable to think that learning Python was essential given its wide usage.
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A team of researchers showed how hackers could feasibly use AI to change malware code and bypass cyber security systems as a result. In order to stay ahead, cyber defense systems need to deploy machine learning algorithms that are just as-or even more-powerful and complex.
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Machine Learning Algorithms are those that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory.
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Without recognizing our weak points, we'll never be able to overcome them. If modern job interviews of Data Scientist have taught us anything, it's that the correct answer to the question. "What's your biggest weakness" is "I work too hard." If we never admit our deficiencies, then we can't take the steps to address them.
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Mention the word "search" to most laypeople and it conjures images of Google and Bing. Mention it to most data scientists and it usually conjures notions of keywords and text retrieval, and maybe a passing reference to open source projects like Elasticsearch, Apache Solr, of they are particularly well-versed-Apache Lucene.
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Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike.
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Artificial Intelligence is evolving day by day it's an algorithm which could not only learn from experience but could also transfer that knowledge from one very specific task to another. In fact, A.I breakthroughs have become sparse, and seem to require ever-larger amounts of capital, data and computing power.
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Deep Learning is a subset of machine learning that deploys algorithms for data processing and imitates the thinking process and even develops abstractions. Deep learning uses layers of algorithms for data processing, understands human speech and recognizes objects visually. Deep Learning is feature extraction which uses an algorithm to automatically construct meaningful features of the data for learning, training and understanding.
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With big data being one of the business intelligence buzzwords of last few years, no wonder that publications on business analytics and dashboard design pile up in bookstores. You must be familiar with those trends and keep up with the developing technology, but who has time to read all those books?
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Artifical Intelligence is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use. In each of these areas however, we've made a lot of progress and that's caught the attention of the popular press.
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Artifical Intelligence is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use. In each of these areas however, we've made a lot of progress and that's caught the attention of the popular press.
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For Data Scientists, mistakes help them become sharper and discover new data trends, but that doesn't mean mistakes in Big Data Analytics are not sometimes quite problematic.
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Data science is a powerful tool and has the ability to transform the world. We are already seeing massive changes to the way industries work in the Western world and data science is powering that change. Data Scientists want is like any other employees: they want a good salary.
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Data science is a powerful tool and has the ability to transform the world. We are already seeing massive changes to the way industries work in the Western world and data science is powering that change. Data Scientists want is like any other employees: they want a good salary.
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A collection of useful mobile applications that will help enhance your vital data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more. Data science and machine learning are evolving with their abilities to transform the world around you.
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A Small Real-World Project for Learning Three Invaluable Data Science Skills. As with most interesting projects, this one started with a simple question asked half-seriously: how much tuition do I pay for five
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It is crucial to ask the right questions and/or understand the problem, prior to beginning data analysis. Below is a list of 20 questions you need to ask before delving into the analysis
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The list of the most in-demand programming languages in banking isn't all that much of a surprise. Most every developer can rattle off the first three or four and may even get the order right. However, knowing which ones will be utilized the most in the future is a much more difficult task.
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Predictions abound about the myriad ways that blockchain will revolutionize the business worldâ??-â??from currency to transportation to banking to law. The most important effect that blockchain will have, however, is the one that gets the least attention.
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The key elements disrupting the insurance industry include the Internet of Things (IoT), wearables, big data, artificial intelligence and on-demand insurance.
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According to IDC, global spending on Artificial Intelligence (AI) and cognitive systems will reach $19 billion by 2018. This is an increase by approximately 54% over the total amount consumed in 2017. Mergers and acquisitions are constantly taking place. 2017 alone witnessed some huge acquisitions like Yahoo was acquired by Verizon, Apple bought Shazam etc. Top consulting company Deloitte predicted that technology acquisitions will accelerate mergers and acquisitions in 2018.
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Hackathons have become a new and efficient way of hiring professionals in areas of data science, AI and machine learning, especially for talent-starved mid-sized to smaller companies.
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A Data Science primary survey was conducted in August through September 2018, where 961 current and past Data Science students from 18 cities in India gave opinions on data science courses they attended.
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The individual Data Science technologies that comes under Artificial Intelligence are all moving forward on different paths at different speeds, but all of those speeds are fast. So before you change careers or decide that your business needs some of that AI let's fly up and see if we can make out a larger pattern that will help us understand where we are and where we're going.
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Data has become massively complex and encompassing. Contextualising insights, obtained from a multitude of sources, requires an expert eye.
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Data visualization is helping companies worldwide to identify patterns, predict outcomes, and improve business returns. Visualization is an important aspect of data analysis. Simply put, data visualization conveys outcomes of tabular or spatial data in a visual format. Images have the power to capture attention and convey ideas clearly. This aids decision making and drives action for improvements.
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There is no doubt that computer science is a very marketable course and talk of the town nowadays. Upon your graduation and computer science knowledge, you are guaranteed of landing a high-paying job. On the same note, this course is very wide. Computer science opens you up to many different career options. You can choose to be a software engineer, data analyst, system administrator, security analyst, and many others.
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The secret is out: Data Science. Machine learning, and other data visualization skills are invaluable tools if you want to work in development. But you didn't need us to tell you that - many of you are already programming away on Python or R, and diving into projects on Kaggle or DataKind.
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Hackathons have become the go-to method for machine learning developers and companies to innovate in tech and build great products in a short span of time. With tens of thousands of developers participating in hackathons across the globe every month, it's a great way to collaborate and work on solving real-life issues using tech.
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Almost two years ago missing safety features in the Uber App contributed to the death of my brother. I am a technical product manager and a data science engineer, I can fix this.
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It is easy to get caught up in the hype of machine learning, artificial intelligence (AI), as the promise of this relatively new technology has been credited with saving everything from our health to our work life and even our planet. Realistically speaking, however, in many ways, we are still a long way off from achieving these promises.
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With the world accepting the fact that every company now is a tech company, no matter what its size or what product or service it offers, hiring the best programming language developers is crucial to survival.
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To illustrate how data is playing a growing role in today's flight booking engines I've broken down play by play how each individual piece of data collected about you can be used, analysed and overlaid with other data sets to paint a picture of who you are, what motivates and drives you to purchase a specific product.
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Talking to CIOs metadata rarely comes up as a critical priority that they are currently addressing. They seem to keep it buried under mission critical analytics and other data management priorities. But with the increasing onset of Big Data initiatives the value of metadata is now quickly coming to the forefront and is surfacing as a critical priority for Big Data success.
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Increasingly, colleges and universities, as well as governments, are using data science to improve the ways educational institutions do everything from recruiting to engaging with students to budgeting.
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Machine Learning and Artificial intelligence (AI) is now considered to be one of the biggest innovations since the microchip. AI used to be a fanciful concept from science fiction, but now it's becoming a daily reality. Neural networks (imitating the process of real neurons in the brain) are paving the way toward breakthroughs in machine learning, called "deep learning."
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This quickstart tutorial will get you set up and coding in Python for data science.
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Artificial Intelligence remains a much misunderstood technology, and many business leaders are either wary of it, or simply don't yet see how it applies to their business. Artificial Intelligence isn't all about robots and driver-less vehicles. Its potential for business transformation is huge, and to keep ahead of the competition you need to be thinking now about how your digital transformation strategy incorporates AI for future growth.
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The Internet of Things (IoT) has entered the corporate building, and it's not always a welcome guest. In 2018, 21% of companies reported a data breach or cyber attack due to unsecured IoT devices, according to a recent Ponemon Institute survey. But what's most alarming is the trend. In the same survey last year, only 16% reported IoT-based attacks.
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Imbalanced classes put "accuracy" out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class.
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The data scientist role is fast becoming the most sought after career of the technology world. We asked top data scientist Jake Porway from The New York Times about how he got his job and his tips for success in the field.
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The data scientist role is fast becoming the most sought after career of the technology world. We asked top data scientist Jake Porway from The New York Times about how he got his job and his tips for success in the field.
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Once the Business Intelligence reports and dashboards have been prepared and insights which are extracted from them, this information becomes the basis for predicting future values. And the accuracy of these predictions lies in the methods used.
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Big Data visualisation is among the utmost important components of working with various Big Data analytics solutions. Once the flow of raw data is represented with images, decision making becomes much easier. In order to meet and exceed the customer's expectations, the tools for Big Data visualization should provide a certain set of features.
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Do you need to have a math Ph.D. to become a data scientist? Absolutely not! This guide will show you how to learn math for data science and machine learning without taking slow, expensive courses.
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So here is the difference between a website and a web-enabled application. It is pretty easy but difficult too as a website is related to a particular page and web app is a portal where you get all the content and the information and you can easily view the web apps through browsers.
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Machine Learning has granted incredible power to humans. The power to run tasks in an automated manner, the power to make our lives comfortable, the power to improve things continuously by studying decisions at a large scale, and the power to create species who think better than humans. This list can go on and on.
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Since its inception, the programming language R has been one of the leading preferences for Data scientists & researchers and statisticians. R is a GNU package which was appeared in late 1993; it is a free software environment for statistical computing. In recent years R's popularity has increased exponentially due to advancements in Data analytics field.
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The Artificial Intelligence environment and the Data that powers it, look very different from a year ago. The predictions below broadly consider the public landscape over the coming year, some of the challenges to consider and how to keep things moving in the right direction.
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The current 'big data' era is not new. There have been other periods in human civilisation where we have been overwhelmed by data. By looking at these periods we can understand how a shift from discrete to abstract methods demonstrate why the emphasis should be on algorithms not code.
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You can choose one of the technologies or programming language from this list to learn in 2018 and can switch to a high paying job in 2018 or 2019.
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Here's what data scientists do, how they work, how to collaborate with them, and how software developers can build up their own data science skills.
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The key to creating real business value from data gathered by the Internet of Things lies with including data scientists at every step of IoT implementation.
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When approaching any type of Machine Learning (ML) problem there are many different algorithms to choose from. In machine learning, there's something called the "No Free Lunch" theorem which basically states that no one ML algorithm is best for all problems.
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In recent years, the ability of data science and machine learning to cope with a number of principal financial tasks has become an especially important point at issue. Companies want to know more what improvements the technologies bring and how they can reshape their business strategies.
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For every organisation, a strong work force is its back bone. The realisation that human capital is now lead to an organization's success, has led to an increased emphasis on effective Recruitment by Artificial Intelligence technology.
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For every organisation, a strong work force is its back bone. The realisation that human capital is now lead to an organization's success, has led to an increased emphasis on effective Recruitment by Artificial Intelligence technology.
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The World could have as many as 250,000 open data science jobs by 2024, and the data science skills gap will find companies scrambling to train or hire talent in the coming years. So, the war for data science talent is real.
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Big Data industry and data science evolve rapidly and progressed a big deal lately, with multiple Big Data projects and tools launched in 2017. This is one of the hottest IT trends of 2018, along with IoT, blockchain, Artificial and Machine Learning.
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Reflection is always helpful (and sometimes entertaining ) I wanted to share my first Python program. I initially picked up Python as an aerospace engineering student to avoid spreadsheets and little did I know how good a decision this would turn out to be.
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During my time in freeCodeCamp, I have read many posts and articles from students without previous coding experience who eventually found jobs as software developers.
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R programming language is data analysis software which is used by analysts, quants, statisticians, data scientists, and others.
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There are many different types of analysis to retrieve information from big data. Each type of analysis will have a different impact or result. The data mining technique you should use, depends on the kind of business problem that you are trying to solve.
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Machine learning has been instrumental in solving some of the important business problems such as detecting email spam, focused product recommendation, accurate medical diagnosis etc. The adoption of machine learning (ML) has been accelerated with increasing processing power, availability of big data and advancements in statistical modeling. Fraud management has been painful for banking and commerce industry.
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Learning data science can be intimidating. Specially so, when you are just starting your journey. Which tool to learn R or Python? What techniques to focus on? How much statistics to learn? Do I need to learn coding? These are some of the many questions you need to answer as part of your journey.
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Programming is an integral part of data science. Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist.
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Tired of getting low accuracy on your machine learning models? Boosting is here to help. Boosting is a popular machine learning algorithm that increases accuracy of your model, something like when racers use nitrous boost to increase the speed of their car.
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Tired of getting low accuracy on your machine learning models? Boosting is here to help. Boosting is a popular machine learning algorithm that increases accuracy of your model, something like when racers use nitrous boost to increase the speed of their car.
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As cyber threats continue to evolve, big data and machine learning are increasingly necessary for a strong cyber security strategy. Will Cappelli, vice president of research at Gartner, says that companies are combining big data and machine learning capabilities as part of a more powerful approach to cyber security.
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It's very common for a customer to see advertisements for products that they may have recently searched for, but also sometimes the marketing emails and pop-up ads are not necessarily suitable with the consumer's interests and may come across as more annoying than actually useful.
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According to Forrester Research, Expect to see major changes in how organizations use AI, the rise of new 'digital workers' and increased competition for data professionals with AI skill.
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Drawing inspiration from natural selection, genetic algorithms (GA) are a fascinating approach to solving search and optimization problems. Little has been done to show a step-by-step implementation of a GA in Python for more sophisticated problems. That's where this tutorial comes in! Follow along and, by the end, you'll have a complete understanding of how to deploy a GA from scratch.
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Various business trends today, such as the use of artificial intelligence and multimedia visual marketing, are connected to the concept of Big Data. Every action internet users take generates a data trail, and the amount of machine-generated data is growing too.
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With the manifold of data science tools in the market, it is certainly a rising challenge for you as a data scientist or a blooming data scientist to sort out the best ones.
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You're probably aware of this by now: data science and data analytics are the skills to have in this day and age.
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:Business Intelligence is providing the right data at the right time to right people so that they can take the right decisions." The term Business Intelligence (BI) revolves around the technology-driven process for analyzing data and delivering actionable information to take brilliant business decisions. Business intelligence tool encapsulates strategies and technologies used by enterprises for data analysis of business information.
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There is a lot of confusion between artificial intelligence and machine learning. Based on some articles that I have been reading, a lot of people still refer to AI and machine learning as equivalent words and utilize them reciprocally.
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There are some key skills that go in a resume which make for a good and professional data scientist.
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The massive upsurge in the availability of data in last few years have fueled this passion for breakthrough innovation. The idea of making human life faster and comfortable is the essence of discovering "Internet of Things".
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This morning I found myself summarizing my favorite bits from a talk that I enjoyed at Google Cloud Next in San Francisco, What's New with TensorFlow?
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Data exhaust or exhaust data refers to the trail of data left by the activities of an Internet user during his/her online activity. An enormous amount of often raw data are created. These data (which can take the form of cookies, temporary files, logfiles etc.) can help to improve the online experience.
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Data exhaust or exhaust data refers to the trail of data left by the activities of an Internet user during his/her online activity. An enormous amount of often raw data are created. These data (which can take the form of cookies, temporary files, logfiles etc.) can help to improve the online experience.
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Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.
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Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.
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These free public datasets for a machine learning cheat sheet for high-quality datasets. These range from the vast (looking at you, Kaggle) or the highly specific (data for self-driving cars).
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As data becomes an ever larger part of work, for many people spreadsheets just are not enough. Programs like Microsoft Excel and Google Sheets are powerful tools, but they have limitations in terms of the amount of data you can work with, the kind of analyses you can do, and the types of charts you can make. When data users reach these limitations, the obvious next step is learning a programming language.
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Tech's biggest companies are placing huge bets on artificial intelligence, banking on things ranging from face-scanning smartphones and conversational coffee-table gadgets to computerized health care and autonomous vehicles.
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Organizations combine vast amounts of information from several sources some public, some proprietary, some from human activity, some from devices to gain unique insights about human behavior and decisionmaking.
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Data mining is sifting through all of the data collected by a business or organization, searching for relationships so that reasonable predictions can be made with regard to behavior. This kind of activity is most common among businesses that deal with consumer behavior of any kind.
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Data scientists, data analysts, and data engineers are in high demand. Here are the big data and data analytics certifications that will give your career an edge.
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Data scientists, data analysts, and data engineers are in high demand. Here are the big data and data analytics certifications that will give your career an edge.
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Social media is just the opposite what it tried to present, as said, leaving everything behind social media didn't come to a period of beating the news behind in any way.
Source: HOB6759
Data scientists are really on a roll right now. For a role that barely existed five years ago, the demand for qualified data scientists has reached a fever pitch in the tech world. As a result, data scientist salaries can be quite lucrative if you end up with the right company or in a top location.
Source: HOB7011
To start your career in Data Science just after completing your higher studies, Finding companies that are willing to take a chance on a younger candidate will be an inevitable filter luckily, several great companies are willing to engage with undergraduates. I evaded this artificial barrier by interning as a data engineer, and working on infrastructure related to the data science team. This gave me valuable insights into the day-to-day efforts of a data scientist.
Source: HOB12930
Guido van Rossum, a Dutch computer scientist, set himself a Christmas project. Irked by shortcomings in other programming languages, he wanted to build his own. His principles were simple. First, it should be easy to read. Rather than sprawling over line-endings and being broken up by a tangle of curly braces, each chunk would be surrounded with indented white space. Second, it should let users create their own packages of special-purpose coding modules, which could then be made available to others to form the basis of new programs. Third, he wanted a short, unique and slightly mysterious name. He therefore called it after Monty Python, a British comedy group.
Source: HOB67404
The rising phenomenon of Big Data, meaning the collection and analysis of vast amounts of data, is something that has been around for a long time. What has changed over the last years, is the way we process that data and how we utilize it in relation to business and society. Thanks to the growth of IoT and the persistent use of social media, we have reached an impressive 2.5 quintillion bytes of data that gets created everyday.
Source: HOB3780
What are some bad programming language practices every programmer needs to be aware of in order to avoid them?
Source: HOB56301
If you are a computer science graduate or someone who is thinking to make a career in software development world or an experienced programmer who is thinking about his next career move but not so sure which field you should go then you have a come to the right place.
Source: HOB6432
DevOps involves infrastructure provisioning, configuration management, continuous integration and deployment, testing and monitoring. DevOps teams have been closely working with the development teams to manage the lifecycle of applications effectively.
Source: HOB15168
A Python developer can make an average of $107,578 per year. That's pretty good, but many companies aren't looking for a "pure" Python developer they're looking to fill roles that require extensive knowledge of Python, but aren't full-time development gigs.
Source: HOB21483
Everybody in this era and age is well-familiar with the power of Big Data and contemporary Machine Learning operations, but few business owners and working professionals seem to have a solid grasp on how to use data to monitor the competition.
Source: HOB3762
Building a cool machine learning project is one thing, but at the end of the day, you want other people to be able to see your hard work. Sure, you could put the whole project on GitHub, but how are your grandparents supposed to figure that out? No, what we want is to deploy our deep learning model as a web application accessible to anyone in the world.
Source: HOB30930
Artificial intelligence is reshaping marketing. AI improves targeting, personalization, and engagement. It gives marketers more time to connect with consumers at the time of need. AI-powered marketing should be a top priority for financial institutions that want to succeed in a hyper-competitive marketplace.
Source: HOB4812
Breaking into the world of Data Science can be tricky, but writing a killer resume gives you a better chance of landing a job in this highly competitive field.
Source: HOB38139
Machine Learning and Big Data are the two popular terminologies and both are revolutionizing business operations and regularly providing lots of new opportunities. Machine Learning is presently more subject to practical, large-scale applications than it has ever been in past. Big data on the other hand, became a thing in 2013, after it was observed that 90 percent of the world's data was generated in past years.
Source: HOB4833
Data is absolutely priceless and the new gold for all businesses. But it is not a cake walk to analyze it as greater things come at a greater cost. With the tremendous growth in data, we need a process to extract useful insights from the raw data.
Source: HOB5031
Most popular programming languages like R and Python offer great value in bringing data science and machine learning jobs to successful completion.
Source: HOB15036
People use deep learning almost for everything today, and the "sexiest" areas of applications are computer vision, natural language processing, speech and audio analysis, recommender systems and predictive analytics.
Source: HOB10743
Having an intuition for how machine learning algorithms work- even in the most general sense- is becoming an important business skill. As Andrew Ng has written: "Almost all of AI's recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B)." But how does this work?
Source: HOB10482
AI is a huge technology. That's why a lot of developers simply don't know how to get started and choose the best programming language. Also, personally, I've met a bunch of people who have no coding background whatsoever, yet they want to learn artificial intelligence.
Source: HOB70131
We all have heard about Machine Learning and how it is changing the face of technology is from social media to virtual assistants like Siri and Alexa, IoT and even automobiles, algorithms analyze terabytes of data and make quick decisions.
Source: HOB6954
Chatbots are becoming the prime conversational interface for customer engagement and customer service. AI-powered chatbots are now able to converse with customers intelligently by extracting intents and entities accurately and responding to the queries through the implementation of advanced technologies like NLU (Natural Language Understanding) and NLG (Natural Language Generation).
Source: HOB6582
As cyber threats continue to evolve, data Science and machine learning are increasingly necessary for a strong cyber security strategy. Data science is the field which includes processes and systems to extract knowledge and insights from data in various forms, which is a continuation of data analysis fields as statistics, data mining, and predictive analysis.
Source: HOB5133
Every computer system uses machine learning as it has refined their performance on a definite task. Machine learning is mainly the overview of algorithms and closely concomitant to computers statistics which mainly focuses on predictive analytics. These algorithms are used in many applications, however, they do not work competently with imbalanced datasets. If you have gone through machine learning and data science, you might have faced problems related to imbalanced data.
Source: HOB4449
The Big data revolution and cloud computing are getting hype and companies are shifting the way they think the way of doing business to incorporate data that will drive their decision-making process. Business models keeps to grow on a constant basis and are presently targeting a system-wide transition based on a data-centric architecture.
Source: HOB3756
Business intelligence software is a tool which is formed to evaluate and present the typical data into comestible reports for understanding. Company is benefited to achieve the specific goals through identifying SWOT (strengths, weakness, opportunities, threats), measure KPIs, looks the relationships between datasets for informed-decisions and also create understanding for spontaneous decisions.
Source: HOB2871
If you have doubt in your mind regarding the future of data science then definitely you are concerned the techniques and tools such as Python, Hadoop or SAS will become outdated or going ahead in a data science course will be beneficial for your career in the long-run or not.
Source: HOB3207
Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates' innate capabilities to unlearn, learn and reinvent themselves throughout their careers.
Source: HOB5508
Data mining techniques have advantages for several types of businesses, as well as there are more to be discovered over time. Since the era of the computer, things have been changing pretty quickly and every new step in the technology is equivalent to a revolution.
Source: HOB2976
The art of making data beautiful is taking the world by storm. Data visualization experts and artists are creating amazing things in the world of data design every single day.
Source: HOB5862
Artificial Intelligence is the branch of computer science which mainly focuses on the creation of intelligent machines that is much more similar to human machines. It has a great impact on the technological industry. Through Artificial Intelligence there is a vast advancement in machines and this advancement in both practical and theory, has made machine learning feasible.
Source: HOB5916
It can't be denied that Big Data is a hot topic in present times. But there are businesses still struggling to shift from concept to execution. The only new thing which allows us to make sense of this information is analytics. The primary goal of analytics is the exploration or application of analytic techniques to large amounts of information in variety of types including unstructured data which comprises of text strings, sound and movie files, documents, images, geo-location data, and documents.
Source: HOB3222
According to a US Department of statistics report, demand for data science and data engineers is projected to grow around 40 percent by 2020. However, lack of talent in this space is likely to pose a bottleneck for companies to boost their revenue and small and start-up companies, in particular, will have to face a hard time competing with global majors.
Source: HOB30948
The field of data is concerned with mining the huge volume of data flowing into the organization's warehouses via effective and proper application of scientific and mathematical skills at the same time.
Source: HOB4647
Data mining is the process through which we analyze the behavior of the customer towards the contribution by the business. New and advanced technologies are also covered rather than the data. It helps you to extract and analyze meaning through expansive sets of data. This data is used in summarizing the valuable information which is to be used for increasing revenue, and also gather information on business or product. In this technological world, business organizations are benefited and they are able to make business decisions which are more definite, which help them to increase their sales and earn more profit. All the new products and services are introduced and all the marketing advertising, data mining improves customer obedience and find hidden profits of the business.
Source: HOB2907
Cloud Computing is the method to deliver computing services over the internet. These computing services consist of storage, servers, databases, software, analytics and more. Cloud Computing is an information technology which allows the access the shared computing sources with minimal management effort. With the help of Cloud Computing, we can utilize computing resources online over the internet without investing money in building and maintaining computing infrastructure.
Source: HOB3579
Cloud Computing is the method to deliver computing services over the internet. These computing services consist of storage, servers, databases, software, analytics and more. Cloud Computing is an information technology which allows the access the shared computing sources with minimal management effort. With the help of Cloud Computing, we can utilize computing resources online over the internet without investing money in building and maintaining computing infrastructure.
Source: HOB2226
Robotic process automation is the new technology for businesses and it has already proved to enhance the efficiency and productivity of enterprises. throughout years of experience of implementing robotic process automation the different fields such as healthcare insurance finance and telecommunications, we are now able to facilitate the effective solution for all the businesses which are facing challenges.
Source: HOB7086
Machine learning is an algorithm or model that learns patterns in data and then predicts similar patterns in new data.
Source: HOB4551
Is computer science a difficult course? Well, most people think so. Being a technical course, it is obvious that most people expect to encounter some difficulties when studying it. However, most people believe that generally, computer science is not that hard. There are some few topics that are very simple to grasp. However, there are other topics that seem to be quite hard.
Source: HOB13761
Artificial Intelligence and Machine learning is a combination of new technologies and new techniques. Machines become more advanced and easily grab the new inputs and perform a task more like a human. Using these advanced technologies, computers are able to process a large amount of data can easily recognize patterns in the data. These smart technologies are able to deal with complexities and easily learn and predict data. The technical industry is therefore enhanced by the advanced algorithms and it improves the computing power and storage.
Source: HOB3870
Artificial Intelligence, or simply termed as AI, as the name suggests, is the intelligence exhibited by the machines. By acquiring intelligence, although artificial, the machines will become capable of working and reacting like humans. Today, the artificial intelligence that exists is termed as narrow or weak AI.
Source: HOB14301
Data scientists and front-end developers were the roles that received the maximum remuneration increases in startups in 2017. Both received an average salary increase of about 25% last year, compared to the year before, according to data shared by Bengaluru-based recruitment firm Belong.
Source: HOB5454
Machine Learning is the subset of Artificial Intelligence and all are the part of computer science. It is a data analysis tool that helps us in automating the tasks. Alternatively, it also provides the machines (computer systems) with the capability to learn from the data, without external help to make decisions with minimum human interference. With the evolution of new technologies, machine learning has changed a lot over the past few years.
Source: HOB5817
Marketing has evolved with new technologies which are a mixture of new science and art. Art helps you to extract the content which is creative and science is where you get to know the facts and figures, what worked and what need to be upgraded. Modern marketing does not follow the old approach, now there are multiple data points to back up data which includes many performances of the applications, data of the customer, figures of the sales, and website analytics. Data visualization showcases data which is of visual format.
Source: HOB3573
How diverse will a lucrative, growing field like data science be in the future?
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Crunchbase lists over 5,000 startups who are relying on machine learning for their main and ancillary applications, products and services today.
Source: HOB33234
A data translator is a conduit between data scientists and executive decision-makers. They are specifically skilled at understanding the business needs of an organization and are data savvy enough to be able to talk tech and distill it to others in the organization in an easy-to-understand manner.
Source: HOB3123
At a recent industry event, I had an unexpectedly heated conversation with an investment banker about the meaning of the word startup in today's world. As we argued, it became clear that there is no longer (if there ever was) a standard definition to indicate what someone means when they use the word startup.
Source: HOB3165
To most Americans, the phrase "Medicare Fraud" brings up images of individuals who cheat the system to collect healthcare when they don't need it.
Source: HOB18663
A new Forrester Research report, Predictions 2018: The Honeymoon For AI Is Over, predicts that in 2018 enterprises will finally move beyond the hype to recognize that AI requires hard work-planning, deploying, and governing it correctly.
Source: HOB8319
It's 2018 now and from last few years, phone interviews also known as the telephonic round is the single most popular way to screen candidates in a programming language job interview.
Source: HOB14136
Leadership-capacity constraints are undermining many companies' efforts. New management structures, roles, and divisions of labor can all be part of the solution.
Source: HOB3387
The society is continuously evolving and there are many enhanced information technologies which will lead to transformation having advanced tools for decision making. For companies, business intelligence system plays an integral part. It helped to resolve all the obstacles such as financial and economic crises and have many complexities which improve the process of decision making through this system. The modern techniques provided by IT companies enhance the power of decision making of all the managerial teams.
Source: HOB3612
In this generation, data is increasing with a great speed and data science has become the most accepted discipline of proficiency. Internet serves you with multiple sources of information on many websites. To make a better use of the data, data science started with statistics. To deal with the issues and provides the solution to the problems, at the later parts algorithms were introduced. The age of digital media and computers has started then. Every data was computerized which became a way to develop artificial intelligence and data analysis.
Source: HOB3039
Data science deals with data and prediction and it is often not obvious what a software engineer has to do with this data-centric or data-driven team.
Source: HOB5964
Let's imagine it's a fine afternoon and you have written even a finer machine learning algorithm for your model and you expect that it will give you correct results. At this point, if you find something's terribly wrong in the prediction then you are in the right place.
Source: HOB7119
My Python education began with the book Automate the Boring Stuff with Python by Al Sweigart, an excellent application-based book with simple programs to do useful tasks.
Source: HOB17154
Data science is basically the application of a combination of mathematical, statistical, analytical and programming skills for the collection, organization, and interpretation of data to allow effective and proper management of the business whose data it is.
Source: HOB33768
The main motive of every business is to earn a profit and correct decisions help you to attain effectively. Every leader of the business takes a number of decisions which have a great impact on the work in multiple ways. Therefore, to become a profitable organization the ultimate goal should be an effective decision. Business intelligence helps in the plans which are implemented successfully which is the foremost need of every business. New strategies and procedure of right planning are implemented by business intelligence. Data is being analyzed, stored and accessed by the technologies of business intelligence.
Source: HOB3363
Data mining is the process of examining a data set to extract certain patterns. Companies use this process to determine the outcome of their existing goals. They summarize this information into useful methods to create revenue and/or cut costs. When search engines are accessed, they begin to build lists of links from the first page it accesses. It continues this process throughout the site until it reaches the root page.
Source: HOB7017
As technology redefines how work is done, finance leaders are having to rethink what constitutes finance talent. The successful finance workers of the future will need skills that are not commonplace in the function today. What are the emerging roles and skill sets of the future finance function?
Source: HOB4164
Nowadays every business has big data, and analyzing it has become a significant tool for every business. It changes the way of marketing strategies like how the product is being marketed across various industries. Either it is a startup or a grown up business, every company is considered to be a business of data. In earning the success you need to have data driven strategies which are really needed. if you seem to have fallen in the statistics do not back out, try and collect data to a huge extent.
Source: HOB2433
Global Big Data Services Market report offers clients the most efficient and dependable insight into the Big Data Services market, ranging across different major players.
Source: RNR3300
While several firms have commenced data analytics initiatives, solely some are victorious. Studies have shown that 70% of data analytics programs fail to understand their full potential and over 80% of the digital transformation initiatives fail. Whereas there are several reasons that have an effect on victorious readying of data analytics, one elementary reason is lack of fine quality data. However, several business enterprises understand this and invest considerable time and energy in data cleansing and remediation, technically referred to as data engineering.
Source: HOB3600
Businesses around the globe have incorporated big data in their operations to make sense of the seeming myriad data generated on a consistent basis. The adoption of big data technology and services has grown at a robust pace among end-use industries. As big data becomes more mainstream, and integration with cloud and artificial intelligence becomes more streamlined, further growth is projected.
Source: HOB11304
Technology could be a extremely dynamic field wherever new advancements keep coming back each day. the info is that the next massive factor concerning this contemporary digital age. Entrepreneurs are speedily adopting data-driven methods to power their net presence.
Source: HOB3114
So how can machine learning help you in building a successful career? Is it possible to have a stable career in machine learning? Few questions trick your mind while choosing your career. But machine learning helps you to overcome as it is an effective tool which has an automatic artificial presence rather than the human mind. Machine learning helps computers to work on their own without any human interaction in this tool. Machine learning also includes multiple learning such as supervised and unsupervised learning and also vast applications. Continuous learning helps you to take multiple and effective decisions at the right time. Machine learning has smart algorithms which are used for the marketing campaigns in many smartphone apps as well as in social media applications.
Source: HOB2718
Gone are the days when banks used to store customer information (such as names, photographs, and specimen signatures) in individual postcard-like data sheets.
Source: HOB3756
Ranked No. 1 overall as the best job in America by Glassdoor, data scientists have quickly risen in prominence and critical importance within organizations.
Source: HOB4398
There has been a rapid change which is progressing in this industry in this era. Artificial Intelligence contains deep neural networks which help you to become capable of gathering and then evaluating the complex data which is more accurate and helps human to speed up. The step of innovation is increasing with speed and the tools are becoming more powerful, cost effective and are easily accessible. Nowadays you do not need a tag of a degree or any kind of specialization to tackle artificial intelligence as these tools are becoming a common toolbox for every developer.
Source: HOB4332
Yet the task description of data scientists is ever-changing. the increase of computing, machine learning, and automatic information assortment means that the character of their work is shifting at a speedy pace. Time-intensive tasks, together with information assortment, have become less primary as they become machine-driven, giving thanks to additional strategic tasks.
Source: HOB4782
Is data literacy necessary in youngsters, the solution is affirmative as a result of data is currently a component of daily life. It's nearly become a responsibility of the typical subject to possess some level of data literacy and understanding. Apparently, this analysis paper argues that young learners have the power to figure with complex data sets if they're supported within the right method and if the tasks are grounded in real-life context.
Source: HOB5199
There is no organization which starts data management having already planned that there is no success. Failure is when they do not meet the expectation and do not implement solutions or not giving their highest probability. Through failure, people lose confidence for the teams potential and business intelligence trust is lost for data and profit. The future use of business intelligence can be affected if the project's value is not maximized. Therefore, the successful project implementation value should be understood and how it is adopted, and staying away from the drawbacks can help you realize the value of data investment.
Source: HOB3150
Internet of Things projects now in this session well be talking about the top seven projects which are outstanding themselves in the Internet of Things domain.
Source: HOB16173
Companies that fail to apply data science will be at a competitive disadvantage, according to Deloitte. Here's how to take advantage of new tools, staffing models, and training strategies.
Source: HOB7686
Looking to embark on a new path as a data scientist? That goal may be worthy, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.
Source: HOB35142
Software, product, and QA engineers are among the 20 fastest-growing roles in the Bay Area, according to Indeed.
Source: HOB4818
As a data scientist - or someone interested in the field - you know the industry is constantly evolving. If you want to remain competitive, you need to keep up with popular trends.
Source: HOB27054
I am going to provide very interesting and useful tips through this blog series which will help students to kick-start their career in Data.
Source: HOB6138
As 2018 comes to a close, it's worth taking some time to look back on the major events that occurred this year in the big data, data science, and AI space. Data security continued to be a major topic in 2018, particularly as the rash of big data breaches continued.
Source: HOB9360
Over the last decades, there is dramatic evolution in data visualization. Several organizations have software which is highly experienced which easily helps to show the huge data which they already have. Data is presented through highly engaging and influencing designs which helps in making the decision makers believe for the logical developments which are made from that data. For users, it draws a response which is impactful which helps them to easily perceive the perceptions which are discovered or presented. Data visualization has got importance across organizations through these capabilities.
Source: HOB4746
To make the system more effective it is necessary to choose the right platform for an IT project. All languages are not the same, either its German, French or Japanese we always choose languages which will suit us, but how do we get to know that which is the best and what will suit us. According to the software that you have planned, there are possibilities that many programming solutions might have misfits. All you need is to choose the best language that fits your software by recognizing that your software does not perform in a sequence which is unpredictable, software is complex or not, what are the hardware and operating platform on which the software should be able to run on.
Source: HOB3804
The idea of transforming digitally is quite divergent and it has progressed and enlarged up from few years. Earlier, the development is technology was not too much the recent years it has evolved and entered the business lexicon. We use to apply advanced technologies to the process which already existed just to increase the efficiency which is like new tools are used to do the same things. Digital industry has evolved and expanded the IT industry as well. To achieve a competitive edge new tools are used to perform the task differently. New technologies are implemented which helps in the procedure more effectively in the marketplace.
Source: HOB4998
The biggest challenge of entering into the digital analytics field is that the digital marketing landscape is really difficult. There are very fewer professionals having the experience of both the worlds which is data science and digital marketing.
Source: HOB8718
Data visualization plays a very important part when it comes to communicating the results to the executives. You may have all your fancy formulae and other mechanics at coming to the results, but the executives want to see the results which make sense to them. From the raw data that you have from the various sources, you can get out with certain statistical properties and the sort of analysis that you do and that can then finally be represented in a really meaningful and very precise visual form.
Source: HOB46167
The Internet of Things made possible to turn everyday devices into sources of raw data for analysis to generate useful insights for business It has also seen that Artificial Intelligence (AI) is making analytics more productive and efficient at workplaces too. Enterprises expect much more data to be generated in the years to come as compared to the data generated today.
Source: HOB6156
Global Cloud Database sales and market share comparison and analysis with an overview of current market status, competition, recent developments, future forecast and major manufacturerâ??s analysis
Source: RNR2586
Big data is getting more and more important in this generation, giving away to big business. Data is easily be stored, analyzed and collected through the big data process. There is a rapid growth in this industry, so the job for big data is also increasing with huge coverage. Either it's a small industry or a huge one, there is a need for a specialist in big data.
Source: HOB9003
There's clearly a shortage of data scientists to help companies use more of their data, so pursuing a career in the field puts today's students at a distinct advantage when it comes to staying away from the unemployment lines after graduation. Data science is fast becoming one of today's most in-demand careers, and in fact, the prescient Harvard Business Review declared data science as the sexiest career of the 21st century six years ago.
Source: HOB15615
Now have you ever come across the term data mining? As the name implies, it is an analytic process used to explore a large amount of data in regards to consistent patterns and systematic relationships between variables. Businesses prefer data mining because it aims to predict. Predictive analyses, on the other hand, refine data resources, in particular, to extract hidden value from those newly discovered patterns. �¢??Data mining + Domain knowledge =>Predictive Analytics =>Business Value�¢??
Source: HOB4581
When I reached out to over 30 CEOs running blockchain startups across the world, I wasn't expecting anyone to respond.
Source: HOB8247
Lessons I learned from a big text-to-speech model flub. There is an apocryphal story in text-to-speech circles about a researcher spending months or years refining their speech-generation model, making the speech samples sound better and better, day after day, only to discover that they'd accidentally been listening to the same audio file the entire time and had merely been attuning themselves to it. That story still sends shivers down my spine when it is retold.
Source: HOB17346
More than 20% of the data will be flowing through enterprise in the actual time within the upcoming years. The growth is coming out of services in the financial department, healthcare, manufacturing, and sectors related to media and entertainment. There is a sudden growth in the real-time data which is essential as stated by the author's report.
Source: HOB2964
Data science covers an unlimited network of topics below its umbrella as well as Deep learning, IoT, AI, and numerous others. It's a comprehensive consolidation of information illation, analysis, formula computation and technology to unravel multifarious business issues.
Source: HOB11304
In machine learning, huge data goes into training models. Many times the model becomes complex so the cost of model training increases at a higher rate. Models of machine learning which are complex can easily be prepared only if you have years of experience. The models are not created very efficiently if the machine learning and artificial intelligence engineers do not have a good experience. This is how to transfer learning is more significant for deep learning.
Source: HOB6390
Another challenge develops when businesses gather data and organize it without considering the questions they will be used when processing the data. Each step in the data collection process should lead toward gathering useful data and analyzing it with the goal of developing actionable Business Intelligence.
Source: HOB5520
The analytics and business intelligence marketplace are crowded as ever. The scope of what constitutes analytics package could be a bit muzzy, and this makes choosing the simplest doable metal tool a frightening task. Modern tools cover way more than heritage reportage, with capabilities starting from information integration and information preparation to information quality, governance, and even machine learning.
Source: HOB3906
Advice on how to be more consistent in your educational journey for data science. Over the last few weeks, I've taken a break from writing to focus on applying to internships. But as I was driving to class today, a question began to bother me.
Source: HOB10836
Since the earliest times, our social rhythm has been set by the natural world. The rise and fall of the sun and moon, the firmament full of stars and other natural phenomena dictated so much early human behavior.
Source: HOB6042
Competitive markets have increased and faced by the media and entertainment companies. There are several ways which can easily bring upon the content of the consumer, operate the need where the operation is done in a better way and more efficiently than ever. Delivering that content means generating more and more revenue they need. Sectors like media and entertainment always deal in data, as it is already known. So is there something new in it?
Source: HOB3099
The majority of artificial intelligence (AI) research to date has been focused on the vision. Thanks to machine learning, and in particular deep learning, we now have robots and devices that have a pretty good visual understanding of their environment.
Source: HOB6579
Robotic process automation is capable of machine learning and artificial intelligence which easily handles the task which is repeated and is of higher volume. This language is coming for which no one is aware of! In this language, maximum software usage with AI is covered, and for handling the high volume and repeated task machine learning has the power to perform what humans require for their performance. To develop the million dollar revenue market, RPA is making its way while no one is looking. For the RPA system, the space for leading startups has raised for more than $300 million.
Source: HOB12102
It's Fascinating, Diverse, Not Magic, Creative, Science and Free! A few years ago, when I was a junior software engineer, I worked on a problem with one of our machine learning algorithm developers.
Source: HOB6939
Q&A sites and data science forums are buzzing with the same questions over and over again: I'm new in data science, what language should I learn? What's the best programming language for machine learning?
Source: HOB19224
Reinforcement Learning (RL) could be a machine learning methodology that empowers a specialist to be told in Associate in Nursing intuitive setting by activity trial and error utilizing observations from its terribly own activities and encounters.
Source: HOB8919
The last century has seen tremendous innovation within the field of arithmetic. New theories are postulated and ancient theorems are created sturdy by persistent mathematicians. And that we are still reaping the advantages of their thorough endeavors to make intelligent machines.
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Data will continue to drive business transformations in 2019. Here are three trends that could change the business landscape over the coming year and tips to help you prepare for them. As we prepare for 2019, it's a good time to look back and reflect on the effect that data has had on global business during this past year and what might be in store in the year ahead.
Source: HOB6015
A holiday reading list condensed into 30 quotes about data science
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No matter where you're at in your career, learning something new skills like a programming language, Design, Communication, AI, ML, DS can only help you.
Source: HOB69282
Deep learning seems to be leading as in data science as it is more researched. There are many applications which can help you build a save future as predicted by the data scientist. Deep learning looks hard and intimidating. Applications like TensorFlow, Keras, GPU based computing might scare you but in reality, it is not too hard and its take time effort and time to follow, and applying these applications in regular problems is easy.
Source: HOB24381
In this article, we are going to study in depth how the process of developing a machine learning model is done. There will be a lot of concepts explained and we will reserve others that are more specific to future articles.
Source: HOB12795
The edge, and edge computing figure conspicuously in an exceeding heap of the internet of things (IoT) discussions straight away. however wherever and what the sting is incredibly quickly obtaining difficult.
Source: HOB7932
Did you know most of the information you consume now was created in the last two years? In the next two years, around 1.7 megabytes of information will be generated every second for every human being in the world.
Source: HOB7974
This year's round-up of predictions from industry figures yields wisdom in Data Science, AI, data regulation, data governance, the state of the Hadoop market, open source and "the edge."
Source: HOB9390
There is a great development in the year 2018 which continue to mark an increase in the data adoption in business. For increasing the data-driven technologies companies are putting their full efforts. For establishing a data culture there are many firms who are investing in the modification initiatives for their organizations.
Source: HOB3366
The convenience of mobility will mainly depend on the performance of the companies together and how the shared data is managed within the ecosystem of mobility. For more than 10 years, mobility as a driver has existed and created new challenges for OEMs which has increased the competitiveness from the players which are nontraditional.
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f you are willing to get into tech, and not knowing which skills you need for your career! By gaining the tech skills you can increase your marketability for the future as well. It is a very broad field as you have multiple directions where you can lead in and there are several skills which are demanding and gives you an appealing journey. Here are some skills which are demanding in 2019
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When it comes to landing a high-paying job, the thing that counts the most is the programming language practical skills you possess.
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Data science covers a vast network of topics under its umbrella including Deep learning, IoT, AI, and various others. It is a comprehensive amalgamation of data inference, analysis, algorithm computation and technology to solve multifaceted business problems.
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A comprehensive guide to interpreting machine learning models. Interpreting Machine Learning models is no longer a luxury but necessity has given the rapid adoption of AI in the industry.
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It's that time of year again when I look into the Crystal Skull...er, ball, and make some predictions of the continuing challenges and new trends I foresee in Big Data and Data Science for 2019.
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Nothing is quite so personal for programmers as what language they use. Why a data scientist, engineer, or application developer picks one over the other has as much to do with personal preference and their employers' IT culture as it does the qualities and characteristics of the language itself. But when it comes to big data, there are some definite patterns that emerge.
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Wanted to build yourself? Yes. These paths help you to eliminate that workload which you have to do otherwise. There are a large number of resources which are overwhelming when you enter into data science. And for that, you have the learning path which gives you a success in the community. We have a complete path of learning to become a data scientist in 2019.
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It is noted that business intelligence and big data can only be used for enterprise companies. But now even small businesses can easily afford to track into the information wealth. It is not easy for small businesses to become data-driven as they also deal with the records which are complex and it is really difficult to process and interpret into insights which your business can actually use.
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Data Science & Cybersecurity, what is big data analytics? Why is machine learning applications so important? Why did InfoSec Professionals require to learn about DS? What to know about "data bots" as a data science professional? Differences in data science vs machine learning? How to crack cybersecurity jobs with data science advantage?
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New digital technologies could bring over $150 billion (Rs10.7 lakh crore) to India's GDP by 2021. So, many Indian professionals are choosing to learn new skills in order to cash in on this economic transformation.
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So these days you have multiple options of an algorithm that can drive a car? A picture can be scanned! Or in a crowd, you can easily find your face! It seems that these days every company is searching some new uses for adapting algorithms as new data is being encountered. It is said that Machine learning drives everything in every company.
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Data Science is easy to learn or not? Can Anyone Be A Data Scientist? No, data science is not easy. It's just unshaped and not professionalized. By this I mean there are no standard sets of tools, no educational curricular, no certifying bodies, nor any specific career paths that lead to becoming a data scientist; yet all the essential bits are there and they're not easy to acquire, assemble or apply well. Yes, one can learn R and Hadoop and claim to be a data scientist, but that's far from the truth.
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According to estimates, 80% of world data is unstructured. This data resides in social media feeds, digital photos, emails, and audio files, to call some. Unstructured data are often onerous to research, which suggests businesses will fail to utilize the treasure hoarded wealth of data they need access to. That is wherever fashionable business intelligence tools are available in.
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Business intelligence can be afforded by anyone not only by the big organizations. Many companies deal with the information which is of a larger amount so there is always a need for business intelligence driven insights, nowadays data decisions are not only made by many companies but also SMEs and entrepreneurs.
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For helping you with getting a better understanding of the technology, a new report is published by TIBCO which shows the data visualization benefits. However, data visualization is crucial to comprehend and obtaining intuitions from the data of large volumes. There are multiple enterprises which really don't know from where to start this technology.
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These days organizations look for the ways where they can prepare the data very quickly and appropriately for solving the challenges of data and enabling machine learning. The data should be cleaned and accurate it should be checked before the data is brought to the model of machine learning or any other project of analytics.
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There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. Now there are three platforms that offer Automated Deep Learning (ADL) so simple that almost anyone can do it.
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Data mining is the method of sorting through massive information sets to spot patterns and establish relationships to unravel issues through information analysis.
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Cloud Data Integration market research report 2018 provides an insight of top players in Global market their market share, sales, revenue and a complete overview of the market competition with industry forecast and analysis.
Source: ReportsandReports2382
Due to the digital transformation, there are many IT jobs what will be more in demand and will be oriented towards high technology like AR, IoT, and artificial intelligence. For all these positions the demand for the workers who are qualified has increased which also includes the front-end developer and back-end developer, and many more positions which are not known. Here are some of the jobs which will be more in demand in the year 2109-2020.
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Natural Language Processing is the ability of a computer program to get understand the human language and it is the component of artificial intelligence. Now it is very important to learn this concept and following are the courses through which we can gain insights and learn this concept thoroughly.
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Not convinced for storing all your data in the cloud? If so, no you are not the only one. This term cloud is very unclear and its really hard to wrap your brain. For making the cloud more real, think cloud to be the safest place to store your data, by using some points
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The process of analyzing worldwide data on animals is obtaining easier, and it all comes right down to data harvest techniques that have become additional accessible the scientists due to Artificial Intelligence systems.
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Humans are granted with incredible power by machine learning. The task is run in an automated manner, it also makes our life comfortable and things are also improved at a large scale and it also has the power to create a tool who thinks well than the humans. This article is mainly to make you aware about the world which is beyond the video tutorials and blogs.
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The Virtual Data Rooms Market report highlights the current market size and future potential of the market at the global and regional level with the help of industry trends and market performance.
Source: RNR1575
On Everyday, every second huge amount of data is being generated on the Internet. As per IBM report, about 2.5 quintillion bytes of data is generated every day.
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The amount of data generated these days from all business domains conjointly called massive data is large, encompassing data gathering, data analysis, and data implementation method.
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Building computers that have the ability to think like humans might be the turning point in today's technology. There is a battle between the worlds leading systems of artificial intelligence which are advanced. The main motive for it is to find if the computer can easily act more like a human or not. It is noticed that technology is not only changing but also raising some questions about what the human work means to be.
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Skills of data analytics have become the leading factor in terms of the advanced development and for the career perspective, the demand of the data analyst is increasing day by day. There are several online courses which you would prefer if you want to build your career as a data analyst as it will help you to learn the fundamentals of data science, the key tools of data science and the study of programming languages in the analysis of big data.
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The selection process of data scientists at Google gives higher priority to candidates with strong background in statistics and mathematics. Not just Google, other top companies (Amazon, Airbnb, Uber etc) in the world also prefer candidates with strong fundamentals rather than mere know-how in data science.
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The rates of unemployment are at a lower phase and the economy is booming. There are many companies who are facing the shortage of data engineers and wanted some professionals with high skills. It is really difficult to get skills for both data scientist and data engineer in the same file while taking the step towards it is the personal choice which profiles you want your career to be in.
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The global huge data market is growing at an unexampled pace. One study from Wikibon showed that the market is growing at a rate of 10.48% a year. Because the price of the large data market continues to grow, structure dependence on that can solidify further. This solely becomes a controversy once data servers become inaccessible or data is accidentally or deliberately destroyed. Enter the data recovery industry.
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This programming language R is developed in 1993 and was R was conceived by professors Auckland University Ross Ihaka and Robert Gentleman. R language has methods of statistics and graphics. And these methods also comprises of the machine learning algorithm, time series, linear regression, interferences of statistical and many more.
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In this world of an academy, technology is becoming more personalized and efficient for students which are due to the advancements in the artificial intelligence. There are many applications which are upcoming and trough this technology our way of learning is getting changed. Education for us is becoming more accessible as these days students have smart devices and computers which make them aware of education.
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Over the past years, it was witnessed that data science is majorly used by everyone and there are rise and fall of some words like big data, business intelligence, analytics and these days artificial intelligence. Today there are many aspiring data scientists who are encouraged in learning the techniques of modeling. There are several books which will help a data scientist to get prepared philosophically.
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Global Data Erasure Solutions sales and market share comparison and analysis with an overview of current market status, competition, recent developments, future forecast and major manufacturerâ??s analysis
Source: RNR1605
Through wide research, there are multiple technologies which are upcoming continuously that have the most persistent auto labeling for creating the training data which is affordable and consumes time. The area of research which is getting the extreme attention is Snorkel.
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All professionals I'm sure looking ahead to a new start and want to increase their data analysis skills. So here is the collection of books through which data scientist can sharpen up their knowledge and skills.
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Some Ebooks which will give enhance your knowledge for free in deep learning, Hadoop and DataViz. Here you have some links to get these free EBooks.
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Across many industries, there are many large enterprises and businesses having the high performance which are tidily turning towards artificial intelligence and new analytics to make the decisions faster, productively and data-driven.
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Continuous intelligence from all your data is not another phrase to describe real-time, speed or throughput. It's about frictionless cycle time to derive continuous business value from all data. It's a modern machine-driven approach to analytics that allows you to quickly get to all of your data and accelerate the analysis you need, no matter how off the beaten track it is, no matter how many data sources there are or how vast the volumes. It's about not doing this once but letting the machine automate it so it's continuous and frictionless.
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Let look upon the learning part of the machine learning, leaving aside the machine part. Do you know from where does machine learn from? It is the Generative Adversarial Network people and many times from each other. If we go in the beginning, even GANs also learn from the data which is provided by humans.
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Cloud Data Integration Market report present the in-depth analysis Production by Region (United States, Europe, China, Japan etc.) Industry Overview, Segment, Type, Split the breakdown data, future trends, Manufacturers and Applications forecasts till 2025.
Source: QYResearch Group1563
