NYU Data Science newsletter – September 3, 2015

GROUP CURATION: N/A

A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data.

Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management.

In the final stretch of a major client project back in 2014, a fellow data scientist at Civis Analytics whispered a modest proposal to me: “We should be able to build all these models from massive amounts of survey data – and actually get some sleep.” A reasonable idea – and one that we ended up solving with the Civis API. While the interface of the Civis platform gives me the tools (which you can use too!) to solve client problems and build workflows, with the API, I can script the Civis platform to process more data and build more models, faster, than I would ever have time to do by hand.

Science is a methodology for creating generalizable knowledge about the observable and natural universe. Often the purpose is pure science, intended to get at causality, or the “why” of the data. Why is the data the way it is?

Data science is more applied, and is more geared to seek the “what” of the data, rather than the “why”. The generalizable knowledge extracted from the data is intended to be actionable for some organization. For example, making predictions about customer behavior (“what” will happen next) can be monetized. Why the customer behaves the way he/she does it less important.

Last Wednesday, “A Neural Algorithm of Artistic Style” was posted to ArXiv, featuring some of the most compelling imagery generated by deep convolutional neural networks since Google Research’s “DeepDream” post.

On Sunday, Kai Sheng Tai posted the first public implementation. I immediately stopped working on my implementation and started playing with his. Unfortunately, his results don’t quite match the paper, and it’s unclear why. I’m just getting started with this topic, so as I learn I want to share my understanding of the algorithm here, along with some results I got from testing his code.

More speed, more data, real time analytics, in-stream analytics. Everything seems to be trending toward size and speed. Take the now almost universally accepted aphorism ‘more data beats better algorithms’. I beg to differ. There are plenty of examples where accepting this rule of thumb has led to shortcuts on the analytic side and lots of bad results.

It’s always good to read about projects gone bad as a reminder that we need to pay attention to the basics of good analytics. Larry Greenemeier gives us a great example of how the widely touted Google Flu Trends analysis that was supposed to be an accurate worldwide predictor of annual influenza trends derived from related Google searches missed the mark by a wide margin.

In line with open access policies, the timely sharing of information on clinical, epidemiologic and genetic features of emerging infectious diseases as well as information on experimental diagnostics, therapeutics and vaccines, is critical for actions during a rapid public health response.

WHO held a consultation in Geneva, Switzerland, on 1-2 September 2015 to advance the development of global norms on data and results sharing in public health emergencies. Government representatives, public health agencies, scientists, research funders, ethicists and industry representatives attended the consultation. Acknowledging the years of work that many groups have engaged in to support data sharing in health research, the following consensus emerged from the meeting specific to the emergency perspective.

Google is working on a new technology called VP10 that will allow it to squeeze higher-quality video over broadband and mobile networks. And thanks to patent issues with a rival standard, it has a chance to catch on.

A group of researchers looked at how people used their phones to figure out when they were bored, then suggested they go read a BuzzFeed article.

Jason Cohen isn’t the first man to look for the solution to his problems at the bottom of a beer glass. But the 24-year-old entrepreneur might be the first to have found it.

Cohen’s tale would make a great episode of HBO’s “Silicon Valley” if only his epiphany had taken place in sun-dappled Palo Alto, Calif., rather than blustery State College, Pa. That Cohen has involved GPUs in this sudsy story should surprise no one.

This is the tale of a man who didn’t master marketing to sell his product — quality control software for beer makers. He had to master it to make his product. The answer, of course, turned out to be free beer. And that’s put Cohen right in the middle of the fizzy business of craft brewing, a business that moves so fast he’s enlisted GPUs to help his software keep up.

This workshop aims to bring together a diverse and cross-disciplinary set of researchers to discuss recent advances and future directions for developing new network methods in statistics and machine learning. In particular, we are interested in

network methods that learn the patterns of interaction, flow of information, or propagation of effects in social and information systems

empirical studies, particularly attempts to bridge observational methods and causal inference, and studies that combine learning, networks, and computational social science

research that unifies the study of both structure and content in rich network datasets

Deadline for Submissions: Monday, November 2

Reflecting our continuing commitment to building mutually beneficial relationships with the academic community, we are excited to announce the 44 students from around the globe who are recipients of the award.

Among the 44: Wojciech Zaremba from New York University.