NYU Data Science newsletter – May 5, 2015

GROUP CURATION: N/A

The key attributes journalists must have—the ability to separate fact from opinion, a willingness to find and develop strong sources, and the curiosity to ask probing, intelligent questions—are still relevant in today’s 140-character-or-less, ADHD-esque society. Yet increasingly, journalists dealing with technical topics often found in science or technology are turning to tools that were once solely the province of data analysts and computer scientists.

While working at Micrel and Touchstone Semiconductor, Javier Solorzano took calls from engineers who couldn’t find datasheets, chip prices or performance models of parts they wanted. Now he’s trying to turn those frustrations into an opportunity.

Solorzano co-founded startup Elektet to create a Web site to help engineers find and manage information about chips they want to use.

A few months ago, I was doing some training on data science for actuaries, and I started to get interesting puzzeling questions. For instance, Fleur was working on telematic data, and she’s been challenging my (rudimentary) knowledge of R. As claimed by Donald Knuth, “we should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil“. So usually, in my courses, and my training, codes are very basic, and easy to understand. But usually poorly efficient. Since I was challenged, to work on very large datasets, we’ve been working on R functions to manipulate those possibly (very) large dataset, and to run some simple functions as fast as possible (with simple filter and aggregation functions).

Children who grow up in some places go on to earn much more than they would if they grew up elsewhere.

Scientists have succeeded in making a clock so precise it could tick for 15 billion years – longer than the age of the Universe – without gaining or losing a second. The new research, described in Nature Communications in April, sets a world record for timekeeping and is a three-fold improvement over the previous record, set by the same clock in Boulder, Colorado, last year.

These are my thoughts about the process of hiring data scientists. By process I mean the actual process of conducting interviews and asking candidates questions. I don’t mean the topic of the questions or the skills you need to look for in data scientists (I covered that earlier here). Many of these thoughts also extend to hiring software developers and beyond, but they are written specifically about data scientists. This piece is the result of my interviewing candidates and being interviewed as a candidate at mid-size pre-IPO companies, large public companies, and small startups. Full disclosure — these are not data-driven, at least not with any large samples. However, I believe they are internally consistent and will result in a better, more equitable hiring process for both those doing the hiring and those seeking a position and help fight against employee churn caused by mismatched hires.

With Apple’s launch of new health tracking tools for the iPhone and medical researchers’ forays into Facebook to recruit clinical trial volunteers, Web and mobile apps are increasingly seen as a new source for health data.

But psychologists are also looking to the Internet as a new source of information about the mind — and an Indiana University researcher is on the forefront of those developing the tools to make it happen.

Why? It’s hard to go beyond incremental maintenance of open-source projects while publishing on deadline. Long thoughts take time.

The goal of this workshop is to bring together mathematicians and data scientists to participate in a discussion of current methods and outstanding problems in data science. The workshop is particularly aimed at mathematicians interested in pursuing research or a career in data science who wish to gain an understanding of this rapidly evolving field and the ways in which mathematics can contribute. Researchers currently working in data science are also encouraged to attend, to share ideas about mathematical methodologies and challenges. A number of experienced data scientists with a variety of backgrounds from academics, national laboratories, and industry (including startups) will be invited. The program will include overview and technical talks, several panels consisting of practitioners with different experience levels, and one or more poster sessions.

Deadline to Apply: late-June, one month before the workshop that takes place July 28-30.