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Data Science Weekly Newsletter
November 6, 2014

Editor's Picks

  • Hacker's Guide To Neural Networks
    You might be eager to jump right in and learn about Neural Networks, backpropagation, how they can be applied to datasets in practice, etc. But before we get there, I'd like us to first forget about all that. Let's take a step back and understand what is really going on at the core....
  • What Statistical Analysis Should I Use?
    The table below covers a number of common analyses and helps you choose among them based on the number of dependent variables and the nature of your independent variables...

Data Science Articles & Videos

  • The Browsemaps: Collaborative Filtering at LinkedIn [PDF]
    This paper presents LinkedIn’s horizontal collaborative filtering infrastructure, known as browsemaps...We also present case studies on how LinkedIn uses this platform in various recommendation products, as well as lessons learned in the field over the several years this system has been in production...
  • Unit Tests for Stochastic Optimization [PDF]
    Optimization by stochastic gradient descent is an important component of many large-scale machine learning algorithms... In this paper we develop a collection of unit tests for stochastic optimization...
  • LinkedIn Had One Of The First Data Science Teams. Now It’s Breaking Up The Band
    Tthe data science team contained two subsections: the product data science team...and the decision sciences team...the social networking company has divided the subsections and stuck them in separate departments. The decision sciences team now reports to the office of the company’s chief financial officer, while the product data science team is now part of engineering...


  • DataKind - Director of Programs
    In this highly visible and impactful role, you’ll oversee our existing DataCorps and DataDive programs and help launch our new In-House Data Science team. You will play a key part in our continued growth through skillful development, direction and technical execution. Our Director of Programs will be located in our New York City offices, working closely with the Executive Director and the Director of Operations to enhance existing programs, design processes, create strategies, and build internal and external data science teams. At times, you will act as liaison to the Executive Director during meetings with external partners and advisors, business development calls, and technical conferences...

Training & Resources

  • Introduction To Principal Component Analysis (PCA)
    Principal Component Analysis (PCA) is a dimensionality-reduction technique that is often used to transform a high-dimensional dataset into a smaller-dimensional subspace prior to running a machine learning algorithm on the data...
  • SVM - Understanding The Math - Part 1
    This is the first article from a serie of articles I will be writing about the math behind SVM. There is a lot to talk about and a lot of mathematical background is often necessary. However I will try to keep a slow pace and to provide in-depth explanations, so that everything is crystal clear, even for beginners...


  • Bad Data Handbook: Cleaning Up The Data So You Can Get Back To Work
    In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems...
    Containing nineteen essays on lessons learned regarding bad data and data analysis work flows, this riveting good read is an excellent companion to more straight-forward texts on specific methodologies and technologies used in data science.......

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