Data Science Weekly Newsletter - Issue 23

Issue #23

May 1 2014

Editor Picks

  • A Weekend With Julia: An R User's Reflections

    First off, I'm not going to talk much about Julia's speed. Everybody has seen the tables and graphs showing how in this benchmark or another, Julia is tens times or a hundred times faster than R. Enough said about machine speed! Let's talk about intuitive appeal, compactness of notation, and aesthetics...
  • Deep Learning for Natural Language Processing

    Presentation, Deep Learning for Natural Language Processing, by Stephen Pulman, University of Oxford and TheySay, at the March 6, 2014 Sentiment Analysis Symposium in New York...

Data Science Articles & Videos

  • How One Woman Hid Her Pregnancy From Big Data
    For the past nine months, Janet Vertesi, Assistant Professor of Sociology at Princeton University, tried to hide from the Internet the fact that she's pregnant — and it wasn't easy...
  • Why building a Data Science Team is deceptively hard
    More and more startups are looking to hire Data Scientists who can work autonomously to derive valuable insights from data. In principle, this sounds great: engineers and designers build the product, while Data Scientists crunch the numbers to gain insights. In practice, finding these Data Scientists and enabling them to be productive are very challenging tasks...
  • What makes an Image popular?
    Hundreds of thousands of photographs are uploaded to the internet every minute through various social networking and photo sharing platforms. Even from the same users, different photographs receive different number of views. This begs the question: What makes a photograph popular? Can we predict the number of views a photograph will receive even before it is uploaded? These are some of the questions we address in this work...
  • The First Rule of Data Science
    “The first rule of Data Science is: don’t ask how to define Data Science.” So says Josh Bloom, a UC Berkeley professor of astronomy and a lead principal investigator (PI) at the Berkeley Institute for Data Science (BIDS)...
  • Twitter Can Now Predict Crime, and This Raises Serious Questions
    Police departments in New York City may soon be using geo-tagged tweets to predict crime. It sounds like a far-fetched sci-fi scenario a la Minority Report, but when I contacted Dr. Matthew Greber, the University of Virginia researcher behind the technology, he explained that the system is far more mathematical than metaphysical...
  • Simpson's Paradox is Back
    The latest issue of the American Statistician has a set of thought-provoking point/counterpoint papers on Simpson’s Paradox, with a tie-in to the controversial issue of causality. (I will not address the causality issue here.) Since I have long had my own thoughts about Simpson’s, I’ll postpone the topic I had planned to post this week, and address Simpson’s...



Training & Resources




  • Managerial Analytics:
    An Applied Guide to Principles, Methods, Tools, and Best Practices

    Recommended by one of our readers, this book is also very well rated on Amazon (4.8 out of 5 stars)...

    "A manager can’t be expected to learn all what a data analyst or a data scientist knows, otherwise he or she becomes one of them. But the manager probably has to work closely with them or even manage them. For managers, no matter what industries they come from, who really want to understand what analytics means to management, this is a must-read book."

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
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