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Data Science Weekly Newsletter
April 14, 2016

Editor's Picks

A Message From This Week's Sponsor

  • Learn Functional Programming from Experts in 12 weeks, Tuition Free.
    DataScience, Inc. is launching DS12, a residency program that will teach 12 qualified candidates functional programming skills and prepare them with the tools necessary to succeed as part of the world's leading data science teams. To learn more and to apply for our inaugural session beginning June 13th, click here

Data Science Articles & Videos

  • The Purpose of Platforms in Data Science
    Little has been written about scaling an organization of data scientists and the organizational support necessary to do data science well, at scale. I’ve spent the past year at Uber working on exactly this question, and almost 5 years working on data science at Uber generally, and I wanted to share some of my learnings on the power of platforms when it comes to data science...
  • Solving 4x4 KenKen Puzzles with Computer Vision
    I wanted to do something that was challenging but not beyond my skills for a first project. As I was browsing college websites for ideas I found a blog post which described the process of solving Sudoku puzzles with computer vision. It seemed a bit too easy and seemed to have been done by many in the past so I wanted to do something more difficult and original. I resolved to solve KenKen puzzles instead...
  • This is how I used machine learning to predict Villanova would win the 2016 March Madness Tournament
    My machine learning model accurately predicted Villanova would win the championship, netted me first place out of 34 in my office pool, 63rd place out of 608 in the Kaggle competition (top 11%) and ~123,000 out of 13 million in ESPN’s overall leaderboard (top 1%). I wrote a post about this before the tournament with the promise that if it worked out, I’d do a full writeup and release the code. I’m thankful to say it all panned out and here we go!...
  • Statistics for Software
    Software development begins as a quest for capability, doing what could not be done before. Once that what is achieved, the engineer is left with the how. In enterprise software, the most frequently asked questions are, “How fast?” and more importantly, “How reliable?”. Questions about software performance cannot be answered, or even appropriately articulated, without statistics...
  • NYC Subway Math
    Apparently MTA (the company running the NYC subway) has a real-time API. My fascination for the subway takes autistic proportions and so obviously I had to analyze some of the data...
  • Data Normalization in Python
    In this post we'll be digging into some MLB payroll data. In particular I'm going to show you how you can use normalization techniques to compare seemingly incomparable data! Sounds like magic? Well it's actually really simple, but I think these little Python scripts will really help you out :)...


  • Snr Data Scientist, Content Science & Algorithms - Netflix - Beverly Hills, CA
    The Content Science & Algorithms team is a world class group of data scientists focusing on the super interesting question of which movies and shows Netflix should create or purchase the rights to. The team seeks to use data science to better understand this problem and build tools to support the content acquisition teams in their decision making. We are looking for experienced individuals who are passionate about data science and enjoy working in a collaborative environment. Members of the team typically work on one or two projects (e.g. predicting movie viewership) over any six month period...

Training & Resources

  • GeoJSON and the Simplest D3 Map Possible
    This video covers: a) Geographic Information Systems, b) JSON Revisited, c) GeoJSON Introduction, d) GeoJSON Object, e) GeoJSON Geometry Object, f) GeoJSON Feature Object, and g) GeoJSON Feature Collection Object...


  • How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics Accessible introduction to the logic of mathematics-sprinkled throughout with recipes...
    "This is the best book about math that I've ever read. This coming from someone who had loved math from a young age, majored in math, and have taught math for several years..."... 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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