In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
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Many people new to data science might believe that this field is just about R, Python, Hadoop, SQL, and traditional machine learning techniques or statistical modeling. Below you will find fundamental articles that show how modern, broad and deep the field is.
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Here are our 6 predictions for data science, machine learning, and AI for 2018. Some are fast track and potentially disruptive, some take the hype off over blown claims.
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Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.
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Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors - usually academics - are more prone to making their books available for free). Many are O'Reilly books freely available. Here we display those most relevant to data science.
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We are now at 91 questions. We've also added 50 new ones here, and started to provide answers to these questions here. These are mostly open-ended questions, to assess the technical horizontal knowledge of a senior candidate for a rather high level position, e.g. director.
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Whether you are learning data mining for the first time or refreshing your memory or catching up on latest trends, these free books will help you excel through self-study.
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The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.
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An archive of all O'Reilly data ebooks is available below for free download. Dive deep into the latest in data science and big data, compiled by O'Reilly editors, authors, and Strata speakers:
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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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Summary: Advanced analytic platform developers, cloud providers, and the popular press are promoting the idea that everything we do in data science is AI. That may be good for messaging but it's misleading to the folks who are asking us for AI solutions and makes our life all the more difficult.
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The value of data-driven Customer Value Management or CVM cannot be underrated. Data and other algorithms/analytics that shape data are an imperative part of customer value management in a telecom company.
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As everyone knows Machine learning studies computer algorithms for learning to do stuff. We might, for instance, be interested in learning to complete a task, or to make accurate predictions, or to behave intelligently.
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Here are our 6 predictions for data science, machine learning, and AI for 2018. Some are fast track and potentially disruptive, some take the hype off over blown claims and set realistic expectations for the coming year.
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Deep changes are underway in how data science is practiced and successfully deployed to solve business problems and create strategic advantage. These same changes point to major changes in how data scientists will do their work. Here's why and how.
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Going somewhere nice for your summer holidays? Somewhere with a nice beach perhaps - Goa, Grand Cayman or Grimsby? Or a bustling city break? Wherever you're going there's sure to be long periods where you'll sit for hours on end with little to do but read, so I thought I'd throw together a few free eBooks for your Kindle to while away the long hours in the airport, in a traffic jam or on the beach.
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Published in 2013, but still very interesting, and different from most data science books. Authors: Ian Langmore and Daniel Krasner.. This book focuses more on the statistics end of things, while also getting readers going on (basic) programming & command line skills. It doesn't, however, really go into much of the stuff you would expect to see from the machine learning end of things.
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It's all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it's beginning to coincide with science fiction. No, we don't have autonomous androids struggling with existential crises - yet - but we are getting ever closer to what people tend to call "artificial intelligence."
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Here is a tutorial I have created (foundations of machine learning and data science for developers) It is based on my insights from the Enterprise AI course and also the Data Science for IoT course which I teach at Oxford University
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Data science has made its way into practically all facets of society - from retail and marketing, to travel and hospitality, to finance and insurance, to sports and entertainment, to defense, homeland security, cyber, and beyond.
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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.
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The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. For more information about this 700+ pages free book and its authors, click here.
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Clear up the confusion of how all-encompassing terms like artificial intelligence, machine learning, and deep learning differ.
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Circa 1997, the reigning world chess champion Garry Kasparov was against an unknown opponent. The opponent was formidable. Garry was not playing a human. He was playing the game with IBM's behemoth supercomputer, Deep Blue.
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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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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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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.
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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.
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