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Data Science Weekly Newsletter
December 5, 2013

Editor's Picks

  • Probabilistic Programming in Quantitative Finance

    Bayesian statistics has many benefits...Most relevant to Quantitative Finance, however, is the fact that you can very flexibly model latent, unobservable processes and how they relate to observable events. As an example, we could model fear of investors. We can’t measure it directly but it certainly has a bearing on market behavior and the stock price...
  • How To Find The Bars That Women Love

    Jetpac City Guides tells you all about the best places in every city to hit, based on analyzing millions of Instagram photos. It uses some pretty cool big data technology to look at the photos, understand what's going on in them (are people smiling? what are they wearing?) and match them to their GPS locations...

Data Science Articles & Videos

  • Predicting Next Year’s Breakout Artists
    At Next Big Sound, we have always been fascinated by the power of data to predict tomorrow’s music stars. Recently we developed an algorithm that creates a list of the emerging artists who are most likely to break out this year. Over time we tweaked this formula enhancing its forecasting ability, to the point that we’ve been able to patent its powers of prediction...
  • Five Stages of Data Grief
    Anyone who works with data knows that far more time is needed to clean data into something that can be analysed, and to understand what to leave out, than in actually performing the analysis curators need to go through something like the five stages of grief described by the Kübler-Ross model. So here is an outline of what that looks like....
  • Scryer: Netflix's Predictive Auto Scaling Engine - Part 2
    In Part 1 of this series, we introduced Scryer, Netflix’s predictive autoscaling engine, and discussed its use cases and how it runs in Netflix. In this second installment, we will discuss the design of Scryer ranging from the technical implementation to the algorithms that drive its predictions...
  • Big Data - Real and Practical Use Cases
    This post explains in a few succinct patterns how organizations can start to work with big data and identify credible and doable big data projects...addressing the following four usage patterns: Data Factory, Data Warehouse Expansion with a Data Reservoir, Information Discovery with a Data Reservoir and Closed Loop Recommendation and Analytics system...
  • Machine Learning via Large Scale Brain Simulations
    Lecture from Andrew Ng of Stanford University: Recent developments in "deep learning" algorithms, mean they can automatically learn feature representations from unlabeled data. These algorithms are based on building massive artificial neural networks, that were loosely inspired by cortical (brain) computations. In this talk, I describe the key ideas behind deep learning, and the computational challenges of getting these algorithms to work...
  • The Astronomical Math Behind UPS’ New Tool to Deliver Packages Faster
    At UPS, the average driver makes about 120 deliveries per day...To figure out how many different possible routes that driver could travel, just start multiplying: 120 * 119 * 118 * . . . * 3 * 2 * 1. Until recently, UPS used a software tool that gave drivers a general route to follow but allowed wide latitude for human judgement along the way. Over the next five years, however, the company will roll out widely a more exacting algorithm designed to steer drivers away from well-worn paths toward often counterintuitive routes calculated to make delivery faster.
  • Rent the Runway: Data Meets the Little Black Dress
    In just four years, the website Rent the Runway (RTR) has parlayed an online designer dress rental start-up into a game-changing fashion-for-the-masses juggernaut with more than 3.5 million members. Part of the site’s success comes from RTR’s access to—and ability to effectively mine—the many gigabytes a day of data its customers provide, from trend preferences to fit feedback...
  • Big Data, Global Diplomacy and Digital Heartbeat
    Yahoo Lecture by by Kalev Leetaru - well-known for his pioneering (and controversial) work using "big data" approach to problems in international relations, including Culturonomics, a theory for using data to predict international events, and a landmark database GDELT: Global Data on Events, Location and Tone.


  • Data Scientist, New York Times

    Working on high impact, real world problems using huge (and somewhat messy) data sets, including billions of transactions, to unlock valuable insights and power new products for the New York Times...

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