NYU Data Science newsletter – April 28, 2015

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Tiny plugin to display large data sets easily

Our EDGAR® Online SEC API provides easy, programmatic access to data from ~19,000 public company filings with the Securities and Exchange Comission. The dataset includes balance sheets, income statements, cash flow plus valuation, profitability, leverage and liquidity ratios. Get the latest data as filings are announced and access 5 years of historic data. Access to the API endpoints and downloads of the data is restricted to kimono labs users with a free API key.

Bumblebee is a platform for building UI interface(s) – it was designed for Astrophysics Data System (https://ui.adsabs.harvard.edu) and its API.

The first “Text By the Bay” conference, a new natural language processing (NLP) event from the “Scala bythebay” organizers, just wrapped up tonight. In bringing together practitioners and theorists from academia and industry I’d call it a success, save one significant and glaring problem.

The interdisciplinary culture of Cornell has fostered collaboration between a mathematician, a computer scientist and a social scientist that reveals how networks explain a range of phenomena in fields from biology to electric power transmission to politics. Their work has helped bring about a revolution in social science, and might even lead to a better understanding of the causes of cancer.

All this was explained in the Charter Day Weekend presentation, “Six Degrees of Separation,” April 26 in Bailey Hall with professors Michael Macy, Steven Strogatz and Jon Kleinberg; and computing and information science alumni Lars Backstrom ’04, Ph.D. ’09, now at Facebook, and Duncan Watts, Ph.D. ’97, now with Microsoft Research. John Guare, who wrote the play “Six Degrees of Separation,” participated via a video clip in which he expressed amazement at how that phrase has become part of the language.

A new interdisciplinary research program from NASA brings together an interdisciplinary team of scientists, including Stanford’s Bruce Macintosh, to devise new technologies and techniques for detecting life on exoplanets.

Last week, I had the pleasure of attending the CHI 2015 conference in Seoul, South Korea. CHI technically stands for Computer-Human Interaction, but it has become a name rather than an acronym in recent years. And CHI’s scope is very broad, it covers many areas that are not strictly part of HCI (Human-Computer Interaction – why use one acronym when you can have two?).

Below, I talk about a few papers that I found particularly interesting. CHI has 15 parallel tracks, so there is obviously no way to see them all. I mostly went to the visualization sessions, but even from those I’m only picking out less than half the papers here, to focus on the really interesting ones.

The question often comes up from folks starting to explore data science, just what is Machine Learning? When I started out it was easy to explain. Machine Learning (ML) was the category of mathematical algorithms like regression, clustering, decision trees, and neural nets used to extract signals from data, aka predictive models. Then came NoSQL and all that changed.

A short history review. The first commercial NoSQL implementation (not counting Google’s first mover efforts) are credited to Yahoo’s implementation of Hadoop in 2008 to improve their search indexing. The first Hadoop developers conference was mid-2008, and early implementation by Facebook, Twitter, and eBay occurred in 2009. This entire explosion in capability is now barely six or seven years old.

So what exactly has ML become? What’s in the box? How do they relate? In an effort to explain this recently, I put the components on this grid.

Today, in Facebook’s latest earnings statement, the social networking giant disclosed that it spent $1.06 billion on research and development in the first quarter of 2015. That’s almost 30 percent of all the revenue that it brought in during the quarter ($3.54 billion).

A few years ago, one foolproof way of saving the battery on you phone was to turn off GPS. You didn’t really need it. At most, it was an added convenience in a few apps.

But it’s time to turn GPS back on. Your location has become one of the best things about your phone, your smartwatch, and every other connected device you carry. Our tech is learning to adapt to us, nestling into every aspect of our lives so it is more responsive, more useful, and more intuitive. This is awesome, and it’s happening because of three things: location, location, location.