NYU Data Science newsletter – July 15, 2015

GROUP CURATION: N/A

At Stitch Fix we have many problems that boil down to finding the best match between items in two sets. Our recommendation algorithms match inventory to clients with the help of the expert human judgment of our stylists. We also match these stylists to clients. This blog post is about the remarkably useful application of some classical statistical models to these and similar problems that feature repeated measurements.

… this montly newsletter is a nice product, covering some of the territory we are

We’re excited to announce the official release of Effective Computation In Physics. This book was written by two Software Carpentry instructors, Anthony Scopatz and myself, Katy Huff. We were enormously inspired by the vision and work of the Software Carpentry community and expanded on that vision in the book to create a “field guide to research in Python.” While examples and more advanced content are presented in the context of research in the physical sciences, the majority of the book will be useful to all researchers doing scientific computation. In book form, we were able to dive in and expand on best practices more deeply and more extensively than is possible in a workshop. We’re extremely proud to have created “SWC in a book” as instructor Daniel Chen recently described it.

DataCamp wants to teach data science skills to a generation of people, and it got a million in seed money to continue developing its online data-science learning platform.

The round was led by Chris Lynch at Accomplice, an early stage venture capital firm in Cambridge, Massachusetts. The company had raised $300,000 in seed money previously

DataCamp is not unlike coding bootcamps such as General Assembly and Codeacademy, but instead of focusing on general coding skills, it concentrates on data science skills

Twitter is hiring a software engineer who specializes in the Deep Learning branch of artificial intelligence.

Though the engineer will join what is by all appearances a rather small team at what’s being called Twitter Cortex, formed from a newly acquired startup, he or she will be the newest addition to New York’s growing A.I. community.

This hour on Where We Live we ask: Is it possible to harness the incredible power of big data while maintaining personal privacy? Guests include: Jim Stodder (Rensselaer Institute of Technology), Viktor Mayer-Schönberger (Oxford Internet Institute),
Oren Etzioni (Allen Institute),
Sara M. Watson (Berkman Center). [audio, 48:46]

The White House just released more than 100 health-related open data resources to keep people more informed. To reinforce our commitment and support President Obama’s Climate Data Initiative, Esri announces the Esri Human Health and Climate Change App Challenge.

Submission Deadline: Friday, August 14

Are you a champion of open science and open data? Mozilla is seeking researchers eager to advance openness in science and data within their institutions.

The Mozilla Fellowships for Science present a unique opportunity for researchers who want to influence the future of open science and data sharing within their communities.

Application Deadline: Friday, August 14

The researcher-in-residence, supported by Bloomberg, will help leverage knowledge transfer and capacity building on Data Science for humanitarian development at a global level while helping to design new data science methods or applying existing methods to help solve pressing problems that face large humanitarian organizations such as UNICEF.

Candidates interested in applying for this role can download and submit an application from http://www.bloomberg.com/company/d4gx/#unicef.

Deadline for applications is 21 August 2015.

The Berkeley Initiative for Transparency in the Social Sciences (BITSS) invites academic researchers to submit proposals to the 2015 Grants for Social Science Meta-Analysis and Research Transparency (SSMART). The main objective of the SSMART grants is to support and encourage important meta-research in the social sciences, with the ultimate goal of strengthening reliability and validity of social science research findings.

Application Deadline: Sunday, September 6

This paper focuses on finding the same and similar users based on location-visitation data in a mobile environment. We propose a new design that uses consumer-location data from mobile devices (smartphones, smart pads, laptops, etc.) to build a “geosimilarity network” among users. The geosimilarity network (GSN) could be used for a variety of analytics-driven applications, such as targeting advertisements to the same user on different devices or to users with similar tastes, and to improve online interactions by selecting users with similar tastes. The basic idea is that two devices are similar, and thereby connected in the GSN, when they share at least one visited location. They are more similar as they visit more shared locations and as the locations they share are visited by fewer people.