NYU Data Science newsletter – May 12, 2015

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Data scientists often come from diverse backgrounds and frequently don’t have much, if any, in the way of formal training in computer science or software development. That being said, most data scientists at some point will find themselves in discussions with software engineers because of some code that already is or will be touching production code.

Pipelining is the decomposition of a larger task into smaller, distinct tasks, so that they may be calculated in parallel. The idea proliferated in computing when CPU technology blossomed, and a fast-but-general model for computing large problems was needed. Like most good ideas, it maps equally well now: our processing resources are now nodes in a cluster, and our datasets are orders of magnitude larger, but the fundamental idea remains the same.

The gaming-analyticsGaming Analytics Innovation Summit was held by Innovation Enterprise in San Francisco on April 29 and 30, 2015. … Industry leading experts shared case studies and examples providing deep insight into how the gaming industry uses analytics and data science.

… Data has become a dirty word in some education circles, seen as a proxy for an obsessive focus on tracking standardized test scores. But some school districts, taking a cue from the business world, are fully embracing metrics, recording and analyzing every scrap of information related to school operations. Their goal is to help improve everything from school bus routes and classroom cleanliness to reading comprehension and knowledge of algebraic equations.

Since IBM launched its Watson Health business unit last month, the company has been busy, announcing a flurry of partnerships and deployments of its cognitive computing software in different sectors of the healthcare industry. Most of these announcements came out at the World of Watson symposium the company recently held in New York City. Here’s a roundup of what Watson has been up to.

At Strata + Hadoop World in London last week, we hosted a showcase of some of the most innovative big data startups. Our judges narrowed the field to 10 finalists, from whom they — and attendees — picked three winners and an audience choice.

Underscoring many of these companies was the move from software to services. As industries mature, we see a move from custom consulting to software and, ultimately, to utilities — something Simon Wardley underscored in his Data Driven Business Day talk, and which was reinforced by the announcement of tools like Google’s Bigtable service offering.

… [Suhail] Doshi, lean and quizzical in a maroon T-shirt and jeans, began his pitch by declaring, “Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong.” Far better to apply Mixpanel’s analytics, which enable mobile-based companies to know exactly who their customers are and how they use their apps. Doshi rapidly escalated to rhetoric—“We want to do data science for every single market in the world”—that would sound bumptious anywhere but on Sand Hill Road, where the young guy in jeans is obligated to astound the middle-aged guys in cashmere V-necks. “Mediocre V.C.s want to see that your company has traction,” Doshi told me. “The top V.C.s want you to show them you can invent the future.”

The Department of Finance and Risk Engineering is delighted to announce the addition of Dr. Andrew Papanicolaou to our faculty starting Fall 2015.