The following table shows how (from a quantitative point of view) related skills contribute to Data Science, broken down per skill and per contributor. It is used to compute the above summary table. For instance, the first row below reads as follows: Monica Rogati lists data science as skill #3 in her LinkedIn profile; she is endorsed by 61 people for data science, and by 106 people for Machine Learning. The machine learning contribution to data science, coming from her, is 106 / 3 35.33. The people listed in the following table are data science pioneers, among the top data scientists according to LinkedIn.

It is the goal of the proposed workshop to bring together researchers from the fields of machine learning and statistical physics in order to discuss the new challenges originating from dynamical data. Such data are modeled using a variety of approaches such as dynamic belief networks, continuous time analogues of these – as often used for disordered spin systems in statistical physics –, coupled stochastic differential equations for continuous random variables etc. The workshop provides a forum for exploring possible synergies between the inference and learning approaches developed for the various models. The experience from joint advances in the equilibrium domain suggests that there is much unexplored scope for progress on dynamical data.

Microsoft wants to let Xbox One gamers trade in digital games as if they were physical copies. A Microsoft survey of players spotted by WindowsCentral on Monday asked whether gamers would be interested in a system where players could sell back their games for 10 percent of their value, given to the player as store credit. The move would essentially mean that players could give up access to previously bought games in exchange for money. This is a major advantage physical copies have over digital copies, where selling media is a breeze. The move would help alleviate one of the major concerns about gaming's shift to digital, particularly in an industry dominated by the used game market.

Replacing the legacy safeguards that have failed to protect networks and applications, machine learning is at the heart of IT's shift in mindset. It's no longer about preventing attacks or focusing on "known threats," but rather identifying potential threats early enough so that they don't have an opportunity to cause real damage. Without the proper tools, it can take companies months to discover a data breach and even more to resolve it. By failing to detect potential threats early, organizations like Home Depot, J.P. Morgan and Ashley Madison put their finances, reputation and relationship with valued customers at risk.

The industry is far from ready to take humans "out of the loop" on most security operations and let machines run our cyber-defenses autonomously. However, machine learning breakthroughs are already making huge strides in reducing compromises as well as easing the burden of security staff in investigating suspicious events. These under-staffed teams are often overwhelmed with massive volumes of alerts, making it tough to weed out false alarms and identify the subtle signs of more sophisticated attacks. Machine learning anti-malware systems draw on deep knowledge of malware from analyzing millions of malicious samples. Cloud computing horsepower and data science techniques help master the "DNA" and "family trees" of malware, so even completely new malware is accurately detected.

"Abstract Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system.

Before organizational leaders begin to apply valuable resources to evaluating the impact of new technology or the value of a new service, it is important they have a feel for the ability of the technology or service to be delivered, managed, and supported over time. Many vendors and integrators champion their ability to solve a problem with a new technology or service but fall short of providing convincing evidence that they can support that problem solving over time and at volume.

March 21, 2016– AliveCor, Inc., the leader in FDA-cleared ECG technology for mobile devices, announced today the appointment of two former Google leads, Frank Petterson and Simon Prakash. Petterson joins AliveCor as the company's Vice President of Engineering and Prakash as Vice President of Products and Design. Together they will drive the development of products that will continue to enable people and doctors worldwide to proactively manage heart conditions, anywhere anytime. They will lead engineers and data scientists to disrupt the standard of cardiac care and support the company's expansion into the new Wearable MedTech space, pioneered by AliveCor. "I am inspired by AliveCor's mission and vision of bringing together healthcare, wearable technology, and machine learning to create the'Wearable MedTech' category and I look forward to contributing to the goal of creating technology that will make a difference in millions of lives around the world," said Frank Petterson, vice president of engineering of AliveCor.

At first, it sounded like a disaster. Walter Sun, whose job was to make sure Bing gave users the most up-to-the-minute information about current events, was seeing a big spike in the number of people searching for facts about some type of plane incident on the Hudson River. People who saw the jetliner come down began tweeting details almost immediately. Others who'd been aboard a passing ferry started sharing online photos of a rescue effort. It quickly became clear that instead of tragedy, a heroic pilot's emergency landing had saved the lives of everyone on board.

Computer scientists at Microsoft have developed a new artificial intelligence platform atop the hugely popular video game Minecraft. Dubbed AIX, the platform hooks into Minecraft and allows the AI to take control of a character and learn from its actions. It's early days for the project; so far, the scientists have been hard at work getting the the AI to learn to climb a hill. It's a simple enough task to program directly, but for an AI that starts out knowing nothing at all about its environment or what it's supposed to be doing, that's a big ask. The AI not only needs to understand its surroundings, but it also needs to figure out the difference between day and night, why walking on lava is probably a bad idea, and when exactly it has achieved its goal via a system of rewards.