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.
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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.
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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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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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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 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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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 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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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.
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Software, product, and QA engineers are among the 20 fastest-growing roles in the Bay Area, according to Indeed.
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Data engineer may be a comparatively new position that is a hybrid of types between a data analyst and a data scientist. Whereas information scientists are reception making and standardization refined machine learning models and alternative kinds of analysis, data engineers shine at manipulating huge amounts of data and guaranteeing the complete huge data code stack will scale to support large workloads.
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In regards to bitcoin, blockchains implications to data science are quite transparent.
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There are a bunch of opportunities available to computer science graduates today.
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More than 97,000 analytics & data science positions remain vacant in India due to the shortage of talent. Here's how to be relevant in the job market.
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Working as a Data Scientist you will analyze data sets which are more complex and large in number as compared to a Data Analyst job.
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As you can see, the most leading sector is the technology industry. If you are planning to make a career in this field, never give a second thought to it just start over it.
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The aim of this article is to provide you information about those key areas where you can actually develop your career.
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Programming for computers may not be more in demand, but coding is most widely in demand within many industries.
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In many of the cases, aspirants do not know about the responsibilities each position carries. I have discussed two such roles here, Statisticians and Data Engineer which are confused a lot by the aspirants.
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Data Scientist stood out the best job in America for the fourth year in a row with Median Base Salary $108,000, Job Satisfaction rating 4.3 out of 5 and no. of job openings- 6510.
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Books are always the best sources to explore while learning a new thing. Reinforcement Learning has finds its huge applications in recent times with categories like Autonomous Driving, Computer Vision, Robotics, Education and many others. Here are some best books on Reinforcement Learning that you can easily find on Amazon
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As a learner in Data Science I also faced huge difficulty in deciding out the best online resource for me. It was frustrating at the starting and there were times when amid confusions I gave up.
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I have also been an enthusiast in the field and had explored many of the resources for the same during the starting of my career. Here, I have organized and presented these skills in a way so to provide you the best in brief.
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Earlier data analytics field was not more in demand but now there is a continuous growth in this field with multiple job opportunities.
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Starting a career in Data Science can be intimidating but with the right decisions and right choices, a beginner can start a career in Data Science and soar to greater heights in the career.
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A Data Scientist is always a person with a win win approach.If he is not having a win win approach than he is not a Data Scientist. The article presents the 7 habits that every successful Data Scientist carries.
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While there are many resources available online providing courses for TensorFlow picking a right one is always a difficult and confusing task for learners. To free you on that part, I have come here with the best online resources on TensorFlow that I have personally found useful.
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Visualizations are a great way to interpret your insights. Right visualizations deliver your insights clearly to your audience while a wrong method of visualization of your data sums all your efforts to zero that you had put up in finding relevant insights out of the data.
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For a beginner who want to start a career in Data Science, a lot of online platforms are available providing a bunch of information about the field and offering several courses but still there is some ambiguity that still lies in the mind of the aspirants about the careers, salaries, growth rate, skills, and the the various positions in Data Science.
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If we see, the demand for programming languages skills like Java and PHP is increasing day by day and the salary offered is also rising rapidly.
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Diversion is the easiest option you could do after your first or many rejections in Data Science interviews but standing positively all over again and trying to crack the next interview in the next chance is the best thing you can ever do.
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A Big Data Analyst is the person in the organization who collects data from various resources, clean it for further processing, converts it into another format which is easy to analyze (also called Data Munging) and finally analyze it for finding hidden patterns and useful information from it (also called insights).
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Business intelligence includes the strategies or the technologies that any business uses to find relevant insights out of the business data. With the help of Business intelligence businesses takes those important decisions that are required by it to grow.
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Big Data demands a cost-effective, innovative solution to store and analyze it. Hadoop is the answer to all Big Data requirements. So, let's explore why Hadoop is so important.
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The data science field is increasing and getting popular day by day. It has attracted thousands of professional interests.
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Here are some books which will help you to boost your knowledge of data science and some of its fundamental tools.
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The 2019 Data Risk Report analyzes a random sample of nearly 800 risk assessments, giving you an inside look at the state of data security.
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This article having six factors that will positively influence the salary of a Data Scientist. So here are six factors through which you can easily optimize your Data Scientist salary.
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This article will provide you some helpful information about the scope of Python professions in Information Technology. Let us begin with an overview of python.
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Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills.
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Data Scientist should have hands-on these data science books which will enhance their in-depth knowledge.
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From series like the Avengers telling us how Artificial Intelligence (AI) can take over the world to pessimists warning that AI is the most dangerous form of technological disruption, such technophobic views push across the idea that robots and algorithms take away your jobs.
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The world of Hadoop and Big Data can be intimidating - hundreds of different technologies with cryptic names form the Hadoop ecosystem.
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As a popular language of programming, Python is simple and easy to learn. It reduces the time of developing an application with its features of easy compilation and simple syntaxes.
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Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts.
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Build neural network models in text, vision and advanced analytics using PyTorch.
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Data's importance is known by everyone now. If it had been even 10 years back, nobody would have thought of this gigantic shift of power.
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Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing.
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Data science can be used for almost anything and everything in today's world, and why not? Every industry is producing and collecting a huge amount of data every day using faster computers.
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Machine learning is a subfield of Artificial Intelligence, in which a computer system is fed with algorithms that are designed to analyze & interpret different types of data on their own.
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Data is a gold mine of insights. It is important to have an integrated information architecture that facilitates better insights on multi-dimensional information to cater to business decision making and important events.
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This capstone project courses will give you a taste of what data scientists go through in real life when working with data.
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The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.
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Edureka's Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge of Big Data and Hadoop Ecosystem tools.
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Machine learning is a subfield of Artificial Intelligence, in which a computer system is fed with algorithms that are designed to analyze & interpret different types of data on their own.
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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.
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The confusion often arises from the fact that there is an overlap with regard to programming skills. Listed below are some of the key differences between Software development and Data Analytics:
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