NYU Data Science newsletter – February 2, 2016

GROUP CURATION: N/A

A while ago a group of researchers (Enrico Bertini among them) published a paper titled “The Persuasive Power of Data Visualization”. I’ve been interested in how to make information graphics convincing since I read this New York Times story, but I somehow assumed that research about the topic was limited and sparse. I was wrong.

Yesterday I met with one of our PhD candidates at the School of Communication of the University of Miami. Her name is Fan Yang. She’s interested in the communication of risk, and in how charts, maps, and infographics can lead to behavioral change.

Fan shared more than twenty papers she is planning to quote in her dissertation.

This course explores the conceptualization and application of computational and data-driven approaches to journalism practice. Students will examine how computational techniques are changing journalistic data gathering, curation, sensemaking, presentation, dissemination, and analytics of content. Methods from text analysis, social computing, automated news production, simulation / prediction / modeling, algorithmic accountability, and content analytics will be applied to real journalistic scenarios. Several assignments, both critical and creative in nature, as well as an integrative final project will serve to underscore the concepts taught and provide practice in producing artifacts of computational journalism.

If you’re looking to hire a data scientist to join your company, you’re not alone. At The Data Incubator, we work with hundreds of companies that are looking to find data scientists from our Fellowship Program. In our experience, candidates usually come from one of two disciplines: computations or statistics.

Candidates with a strong science or math background usually have had rigorous statistical training in distinguishing between signal and noise and can tell when they are “overfitting” a complex model. Those with a computer science background frequently have the software engineering chops to handle large amounts of data by taking advantage of parallel and distributed computing. While all data scientists need to be functional in both, we’ve found that people coming from each of these backgrounds have quite different strengths and weaknesses. So which type of background should you look for when hiring? That will depend on your business — and whether you’re hiring for a digital or non-digital department.

It’s hard to recruit data scientists. But once you have them, where should you put them? What is the best way to unleash their value? Every org structure has trade-offs. Let’s walk through a few possibilities and explore their pros and cons.

Data scientists are trained to handle uncertainty. The data we work with, no matter how “big” it may be, remains a finite sample riddled with potential biases. Our models tread the fine line between being too simple to be meaningful and too complex to be trusted. Armed with methodologies to control for noise in our data, we simulate, test and validate everything we can. A great data scientist develops a healthy skepticism of their data, their methods and their conclusions.

Then, one day, a data scientist is promoted and presented with an entirely new challenge: Evaluating a candidate to become a member of their team. The sample size drops fast, experimentation seems impractical, and the biases in interviewing are orders of magnitude more obvious than those we carefully control for in our work. We will try to outline the goals of a new process, describe its underlying principles, and walk through the implementation of hiring senior talent.

MIT researchers have developed a machine-learning system that can comb through repairs to open-source computer programs and learn their general properties, in order to produce new repairs for a different set of programs.

The researchers tested their system on a set of programming errors, culled from real open-source applications, that had been compiled to evaluate automatic bug-repair systems. Where those earlier systems were able to repair one or two of the bugs, the MIT system repaired between 15 and 18, depending on whether it settled on the first solution it found or was allowed to run longer.

A group of database researchers meets periodically to discuss the state of the field and its key directions going forward. Past meetings were held in 1989,6 1990,11 1995,12 1996,10 1998,7 2003,1 and 2008.2 Continuing this tradition, 28 database researchers and two invited speakers met in October 2013 at the Beckman Center on the University of California-Irvine campus for two days of discussions. The meeting attendees represented a broad cross-section of interests, affiliations, seniority, and geography. Attendance was capped at 30 so the meeting would be as interactive as possible. This article summarizes the conclusions from that meeting; an extended report and participant presentations are available at http://beckman.cs.wisc.edu.

The Summer Institute in Survey Research Techniques is a teaching program of the Survey Research Center at the Institute for Social Research. It is located on the central campus of the University of Michigan at 426 Thompson Street in Ann Arbor. The summer courses are select offerings from the Michigan Program in Survey Methodology, and can be used to pursue a doctorate, master of science and a certificate in survey methodology.

Ann Arbor, MI. Deadline for completed application is Sunday, May 15. Applicants for Summer Guest admission will be reviewed on the basis of their qualifications and experience. Summer Guests must complete both the enrollment process with the University of Michigan Registrar’s Office and with the Summer Institute.

The Moore-Sloan Data Science Environment Summit is an annual meeting between the data science centers at New York University, the University of California at Berkley and the University of Washington. We spoke with Daniela Huppenkothen, a Data Science Fellow at NYU’s Center for Data Science about her experience at the 2015 summit.

Can you tell us about your work and experience as a Data Science Fellow at NYU?

I spend my time working at the Center for Data Science and the Center for Cosmology and Particle Physics, where I’ve been studying statistical and machine learning methods in Astronomy. At NYU, I found a combination of experts in data science methodologies, and domain experts in Astronomy and Physics.