NYU Data Science newsletter – August 3, 2015

GROUP CURATION: N/A

We need new words to describe the coming wave of machine-generated information.

Software Carpentry was pleased to present a full two-day workshop during the recent SciPy Conference tutorials. The entire conference was recorded, including all sessions of our workshop:

Shell

Python

Git

Scientific Python

Recently I landed a job at URX through a data science job placement program for people with quantitative PhDs called Insight Data Science. As part of a new initiative within the program, I was offered the opportunity to work with URX on a unique data science challenge that held real business value. The goal was to develop an Named Entity Recognition (NER) classifier that could be compared favorably to one of the state-of-the-art (but commercially licensed) NER classifiers developed by the CoreNLP lab at Stanford University over a number of years.

Today we announced that the Google Translate app now does real-time visual translation of 20 more languages. So the next time you’re in Prague and can’t read a menu, we’ve got your back. But how are we able to recognize these new languages?

In short: deep neural nets.

The emergent field of data science is a critical driver for innovation in all sectors, a focus of tremendous workforce development, and an area of increasing importance within science, technology, engineering, and math (STEM). In all of its aspects, data science has the potential to narrow the gender gap and set a new bar for inclusion. To evolve data science in a way that promotes gender diversity, we must address two challenges: (1) how to increase the number of women acquiring skills and working in data science and (2) how to evolve organizations and professional cultures to better retain and advance women in data science. Everyone can contribute.

Do social movements actively shape the opinions and attitudes of participants by bringing together diverse groups that subsequently influence one another? Ethnographic studies of the 2013 Gezi uprising seem to answer “yes,” pointing to solidarity among groups that were traditionally indifferent, or even hostile, to one another. We argue that two mechanisms with differing implications may generate this observed outcome: “influence” (change in attitude caused by interacting with other participants); and “selection” (individuals who participated in the movement were generally more supportive of other groups beforehand). We tease out the relative importance of these mechanisms by constructing a panel of over 30,000 Twitter users and analyzing their support for the main Turkish opposition parties before, during, and after the movement. We find that although individuals changed in significant ways, becoming in general more supportive of the other opposition parties, those who participated in the movement were also significantly more supportive of the other parties all along. These findings suggest that both mechanisms were important, but that selection dominated. In addition to our substantive findings, our paper also makes a methodological contribution that we believe could be useful to studies of social movements and mass opinion change more generally. In contrast with traditional panel studies, which must be designed and implemented prior to the event of interest, our method relies on ex post panel construction, and hence can be used to study unanticipated or otherwise inaccessible events. We conclude that despite the well known limitations of social media, their “always on” nature and their widespread availability offer an important source of public opinion data.

Bell Labs pioneered some of the most important inventions of the 20th century, what was it like to be part of that? Professor Brian Kernighan was there.