Because all of science itself will soon become data that can be mined, the imminent revolution in Data Science is not about mere'scaling up', but instead the emergence of scientific studies of data analysis science-wide. In the future, we will be able to predict how a proposal to change data analysis workflows would impact the validity of data analysis across all of science, even predicting the impacts field-by-field. Drawing on work by Tukey, Cleveland, Chambers and Breiman, I present a vision of data science based on the activities of people who are'learning from data', and I describe an academic field dedicated to improving that activity in an evidence-based manner. This new field is a better academic enlargement of statistics and machine learning than today's Data Science Initiatives, while being able to accommodate the same short-term goals.

The Internet of Things (IoT), with its ubiquitous sensors and streams of big data for big insights, has an estimated market valuation of 1.7 trillion. Apparently, the "sensoring" of the world is a seriously big deal, generating insights into people, processes, and products on a scale that is almost incomprehensible. Certainly, 1.7 trillion is almost an incomprehensible figure. The corresponding forecasts for data-driven insights that lead to such a valuation are expected to be on a similarly large scale to justify those astronomical projections. But insights are not hardcoded within Raspberry Pi or Arduino kits, though IFTTT (If-This-Then-That) kits might be a satisfactory solution (more about that later).

This blog post is authored by Brandon Rohrer, Senior Data Scientist at Microsoft. The raw stuff of data science is a collection of numbers and names. Measurements, prices, dates, times, products, titles, actions--everything is fair game. You can use images, text, audio, video and other complex data too, as long as you have a way to reduce it to numbers and names. The mechanics of getting data can be quite complex. But this guide is focused on the data science, so I'll leave that topic for another time. Data science is the process of using names and numbers to answer a question.

Summary: News of the coming great extinction has the chattering classes agog with fear. The rapid evolution of algorithms, software, and robots will make many kinds of jobs as extinct as the Great Auk. This will reshape the world into a wonderland -- or unleash disastrous social turmoil. Yet another of these coordinated-looking propaganda barrages warn us of the danger. These headlines are correct, but about the wrong subject.

In case you haven't heard yet, Data Science is all the craze. Courses, posts, and schools are springing up everywhere. However, every time I take a look at one of those offerings, I see that a lot of emphasis is put on specific learning algorithms. Of course, understanding how logistic regression or deep learning works is cool, but once you start working with data, you find out that there are other things equally important, or maybe even more. I can't really blame these courses.

Matthew Finnegan and Christina Mercer recently wrote in ComputerWorld UK, "With businesses increasingly keen on incorporating artificial intelligence into their operations, machine learning – the ability for a system to learn from large data sets rather than following preset rules – offers a number of benefits. This might mean building predictive models for fraud prevention, for example, or personalising content on a website. The opportunity is not lost on many of the major tech firms Google, Microsoft, IBM and AWS all offer machine learning capabilities. Here are some of the top machine learning tools to get started with artificial intelligence in the enterprise."

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors.

Huba Gaspar, Global Marketing Manager at Gravity R&D outlines one of the hottest tech buzzwords Deep Learning (DL) and possible ways in which DL based approaches can revolutionize personalization technologies. Deep learning is a sub-field of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence. On a basic conceptual level, deep learning approaches share a very basic trait. DL algorithms interpret the raw data through multiple processing layers. Each of these layers takes the output of the previous one as its input and creates a more abstract representation of it.

The ancient strategy game of Go may have met its ultimate match. The brain-taxing board game is a little like an Eastern version of chess, except many times more complex. It has millions of devotees in China, Korea and Japan. Many of them tuned in today to watch an artificial intelligence computer built by Google's DeepMind beat the world champion, Lee Sedol, in the first of a five-game contest. Duels like these don't come often.

Engineers at Pinterest constantly create new artificial-intelligence algorithms to help its users find what they're looking for among billions of pictures of food, products, houses, and other items. Matching search queries with relevant images is crucial to keep users coming back. But until last year, it could take days to test the effectiveness of each new algorithm. To fine-tune its machine learning and provide better search results faster, Pinterest turned to an unexpected source: human intelligence. It hired crowdsourcing companies such as CrowdFlower to marshal people to quickly do "micro-tasks" such as labeling photos and assessing the quality of search results.