Ben Hamner, Kaggle co-founder and CTO, held a Quora Session last month answering questions on the future of Kaggle, machine learning and AI, and data science workflows.
Source: Kaggle
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Google's Kaggle platform will be playing host to a Google and NCAA co-sponsored competition centering around the college basketball tournament formally known as March Madness. Basically who are not aware, Kaggle is a dedicated platform for sharing data sciences and machine learning expertise, parallel learning about the topic and competing for prizes.
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As an organizer for a data science meetup group, I am often asked this question.
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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.
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Annual salaries for data scientists and machine learning engineers vary significantly across the world. Based on a 2017 Kaggle survey of data professionals, countries with the highest paid data scientists and machine learning engineers (in USD) were: US ($120K), Australia ($111K), Israel ($88K), Canada ($81K) and Germany ($80K). Countries with the lowest annual salaries were: Brazil ($35K), Poland ($29K), Ukraine ($25K), India ($14K) and Russia ($13K).
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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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AI has already changed out lives today. Currently, we already have AI pervading our software systems, particularly those that we, as consumers, use everyday.
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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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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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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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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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Data Analytics tools, latest technologies, and programming languages are required by data science which helps the data scientists to gain meaningful insights and value from the data. A recent survey of nearly 24,000 data professionals by Kaggle revealed that Python, SQL, and R are the most popular programming languages. The most popular programming language was Python 83% used. 3 out of 4 data professionals recommended Data Scientist should learn Python programming language.
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Online code repository GitHub has pulled together the 10 most popular programming languages used for machine learning hosted on its service, and, while Python tops the list, there are a few surprises.
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Data scientists are in high demand and short supply, but they may not need a degree in computer science to get a job, according to a new report from Kaggle.
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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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For enterprises, machine learning and artificial intelligence can help reduce game-changing solutions. In this short article, we are going to talk about things that senior IT leaders should understand in order to launch and sustain a solid machine learning strategy.
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