NYU Data Science newsletter – September 22, 2016

GROUP CURATION: N/A

The department has gone from 17 concentrators in 2008 to 199 as of December 2015, according to the College Handbook and the Statistics Department’s internal records.

… “Historically, sports provided understandable examples of basic statistical concepts,” said Rebecca Nugent, teaching professor and director of undergraduate studies in Carnegie Mellon University’s Department of Statistics. “In recent years, however, the demand for complex statistical analyses in sports has exploded, with teams and leagues collecting massive amounts of data on every player in every game. It’s a whole new ballgame—pun intended!”

Earlier this month, undergraduate students in the Dietrich College of Humanities and Social Sciences’ statistics major and members of the Tartan Sports Analytics Club (TSAC) explored how statistics and sports go hand in hand at a Pittsburgh Pirates game.

“The challenges facing analysts in today’s professional sports world are extremely similar to the types of problems that our statistics undergraduates work on every day: analyzing large, complex datasets with modern statistical methodology,” noted Sam Ventura, a visiting assistant professor in the statistics department and faculty adviser of TSAC. Ventura also works as an analytics consultant for the Pittsburgh Penguins.

The consultancy firm Accenture is patenting a system that would allow an administrator to make changes to information stored in a blockchain. In an interview with the Financial Times, Accenture’s global head of financial services, Richard Lumb, said that the development was about “adapting the blockchain to the corporate world” in order to “make it pragmatic and useful for the financial services sector.”

More Blockchain:

Blockchain: Basics and Hacks (September 20, The Big Data Institute)

Blockchain beyond Bitcoin (September 21, Raconteur Media, UK)

To avoid bias and improve transparency, algorithm designers must make data sources and profiles public.

“A Blockchain is a digital platform that hosts a digital ledger of transactions and shares it among a distributed network of computers. The cryptography technology allows each participant on the network to manipulate the ledger in a secure way without the need for a central authority.”

More Blockchain:

Is an Editable Blockchain the Future of Finance? (September 20, MIT Technology Review, Jamie Condliffe)

Blockchain beyond Bitcoin (September 21, Raconteur Media, UK)

To improve the quality of psychology research—and earn back public trust—a team of researchers has come up with a system to test influential psychology experiments for reproducibility. Dan Simons and his colleague Alex Holcombe created Registered Replication Reports, which allows researchers to nominate and re-examine original papers that had big cultural impact. So far, the hit rate hasn’t been great: Out of five well-known experiments that were re-tested, four resulted in data that strayed from the original findings. [audio, 12:09]

Someone sent me this article by psychology professor Susan Fiske, scheduled to appear in the APS Observer, a magazine of the Association for Psychological Science. The article made me a little bit sad, and I was inclined to just keep my response short and sweet, but then it seemed worth the trouble to give some context.

I’ll first share the article with you, then give my take on what I see as the larger issues. The title and headings of this post allude to the fact that the replication crisis has redrawn the topography of science, especially in social psychology, and I can see that to people such as Fiske who’d adapted to the earlier lay of the land, these changes can feel catastrophic.

We’re very happy to share our new paper on Responsible Data in Agriculture, commissioned by Global Open Data in Agriculture and Nutrition (GODAN), and co-authored together with Lindsay Ferris.

IN 1962 Jacob Cohen, a psychologist at New York University, reported an alarming finding. He had analysed 70 articles published in the Journal of Abnormal and Social Psychology and calculated their statistical “power” (a mathematical estimate of the probability that an experiment would detect a real effect). He reckoned most of the studies he looked at would actually have detected the effects their authors were looking for only about 20% of the time—yet, in fact, nearly all reported significant results. Scientists, Cohen surmised, were not reporting their unsuccessful research. No surprise there, perhaps. But his finding also suggested some of the papers were actually reporting false positives, in other words noise that looked like data. He urged researchers to boost the power of their studies by increasing the number of subjects in their experiments.

Wind the clock forward half a century and little has changed. In a new paper, this time published in Royal Society Open Science, two researchers, Paul Smaldino of the University of California, Merced, and Richard McElreath at the Max Planck Institute for Evolutionary Anthropology, in Leipzig, show that published studies in psychology, neuroscience and medicine are little more powerful than in Cohen’s day.

Los Angeles is implementing the latest IoT (Internet of Things) technology onto its streetlights to monitor noise as a way of gathering various data points from around the city.

In many ways, open sourcing access to A.I. represents the culmination of the “API economy,” where software developers set aside their not-invented-here pride of creation and instead access the work done by others — and in the process accelerate their own development work — via API hooks. If software development-by-API made sense in the past, it may be the only practical path forward when it comes to fully leveraging new capabilities in the artificial intelligence field.

Indeed, the impact of these moves can’t be overstated. On a business level, they significantly lower the barrier for entrepreneurs looking to start up the next wave of artificial intelligence-driven companies by democratizing access to machine learning resources. That’s hugely significant.

“Amazon bills itself as “Earth’s most customer-centric company.” Yet its algorithm is hiding the best deal from many customers.”

Help an NYU data and public health researcher Rumi Chunara from home while learning what makes you sick. [video, 2:00]

  Upcoming No-Cost Data Science Events at NYU and in NYC

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  New Student Interviews: Zhanna Zhanabekova
  
  
  on September 21
  What drew you to the Master of Science in Data Science program?

  As an economist, I think that the union of economic reasoning and data science can lead to more efficient ways of analyzing markets, understanding economic behavior, and conducting academic research. I wanted to go beyond the traditional economics education, and I felt that the Master of Science in Data Science program would be a great way to merge my interests in economics, big data, and artificial intelligence.
  

Make visualisations using only markdown

“a set of architectural guidelines for building a platform to expose analytics as services for enterprise solutions.” Includes a review of open source components.