NYU Data Science newsletter – July 23, 2015

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Major flaws in two massive trials of deworming pills show the importance of sharing data — which most scientists don’t do.

Top Stanford researchers teach efficient and scalable methods for extracting models and other information from very large amounts of data. Next session of this great course starts Sep 12 on Coursera and is free.

George Washington University’s Peter Bock, the Defense Advanced Research Projects Agency’s Paul Cohen, and MIT’s Andrew McAfee join Amy Alving, former chief technology officer of Science Applications International, to discuss recent innovations in artificial intelligence as well as the economic and security implications of these technological advances.

Big data has been breaking into MBA courses everywhere but a new generation of specialist masters programs dedicated to business analytics have emerged, fuelled by a relentless rise in demand for data analysis.

Stripping out some core modules to focus on data, a host of top business schools have established programs for the age of machine intelligence. NYU Stern, USC Marshall and Melbourne have all launched data degrees. Scores more, including Chicago Booth, HEC Paris, and Berkeley’s Haas School are mining data within their MBA programs, as firms look to unlock value with analytics.

“The demand for analytics-driven outcomes continues to increase,” says Prith Banerjee, managing director of global technology R&D at Accenture, in an interview with BusinessBecause.

Curious passersby press their faces up to my lab’s window in lower Manhattan as our rotating table swings into action.

“What are you doing?” someone mimes.

I point to our video wall, which explains our work. Here in our geophysical fluids lab at New York University, we study the basic principles of how the Earth’s fluids behave. Out in the field, in Greenland and Antarctica, we gather data that we use to build ever more accurate computer models.

My research focuses on global sea level change. If the main ice sheets of Greenland and Antarctica even partially disintegrate, the consequences for society are immense – they hold about 70 meters of potential sea level rise. Even a relatively modest amount of disintegration would put places like coastal Florida, New York City or Abu Dhabi partially underwater. In fact, we’ve established a global sea level research center at NYU Abu Dhabi. The goal of all our activities is to gain better knowledge of how ice sheets and oceans interact. To tease apart the puzzle, we need to travel to where the ice is and collect data.

Organizations all around the world are asking themselves how to build a data culture within their walls. Of course, this means something different for each of them. However, I want to introduce you to my process for answering that question. I rely heavily on Beth Kanter’s amazing work in this space, specifically her book Measuring the Networked Nonprofit (co-written with KD Paine).

There are three guiding questions you can use to lead you through this process. I’ll go into each one in detail in this blog post.

What is a data culture?

What is our existing data culture?

How do we build a data culture?

Over the course of the last year I’ve spoken with hundreds of employers interested in hiring data scientists, in particular, data scientists with advanced educational degrees. Many employers and hiring managers have heard that big data is the “hot new thing.” But as with all “hot new things,” there’s as much misinformation about data science as there are facts. Here are three misconceptions about big data and data science that I often encounter:

1. Big data is statistics and business intelligence with more data. There’s nothing new here.

… what can we do to ensure that innocent people are not misidentified again? One solution is to identify people who are much better than average on tests of face recognition. It is inferred that about 1%-2% of the population are extremely good at recognising faces. These so-called “super recognisers” can remember about 80% of the faces that they encounter in daily life. Security services are currently looking for such people to help them identify people from CCTV or pick people out of large crowds.