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Data Science Weekly Newsletter
January 21, 2016

Editor's Picks

  • Why video games are essential for inventing artificial intelligence
    Having concrete problems to try to solve with AI is necessary in order to make progress; if you try to invent AI without having something to use it for, you will not know where to start. My chosen domain is games, and I will explain why this is the most relevant domain to work on if you are serious about AI...
  • R Users Will Now Inevitably Become Bayesians
    There are several reasons why everyone isn’t using Bayesian methods for regression modeling. One of these reasons has recently been shattered in the R world by not one but two packages: brms and rstanarm...

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Data Science Articles & Videos

  • The Unreasonable Reputation of Neural Networks
    It is hard not to be enamored by deep learning nowadays, watching neural networks show off their endless accumulation of new tricks. There are, as I see it, at least two good reasons to be impressed...
  • Analyzing Canada-US Border Crossing Data
    Recently I found an open data source containing lots of interesting attributes from the last 9 years from the four major border crossings here on the West Coast. For each crossing, the dataset had Volume, Delay, Service Rate and Queue Length attributes for each lane type (cars, trucks, buses, NEXUS) in 5-minute intervals. I decided to look at three different questions...
  • T-Shirts Unravelled
    We washed, dried, measured and weighed 800 of the most popular men's t-shirts available online. The shirts included a wide variety of price points ($5-$50), sizes (XXS up to 6XL) and fits ("slim", "tall", "relaxed", etc.). After compiling the data, we worked with beta testers in NYC to develop an algorithm that could recommend t-shirt brands and sizes for a wide range of body types...
  • Cash for A.I. startups
    Interview with Shivon Zilis of Bloomberg Beta on the emerging wave of machine intelligence startups...
  • Understanding Deep Convolutional Networks
    Deep convolutional networks provide state of the art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and non-linearities. A mathematical framework is introduced to analyze their properties...
  • How should a Data Scientist's resume differ from an Academic CV?
    Your academic cv is very coursework and research focused. You've heard business resumes need to be more action and results oriented, but you're not sure what that means for you. You're looking for advice on how to re-work your academic cv and not finding much advice out here. To help get you started, here are some thoughts on what you'll need to do... ...


  • Data Scientist - Bitly - New York
    Bitly faces a variety of interesting challenges that are ideally suited for a data scientist to pursue. We see massive amounts of data giving us a fascinating view into what is happening on the internet. With this data, it is our mission to empower marketers to make better decisions by providing insight into the connected world...

Training & Resources

  • How to Make the Leap from Excel to SQL
    Blog post designed for Excel users who are looking to learn some SQL. We walk through how people can translate their Excel knowledge to SQL, and we've included a free workbook of six go-to Excel functions and their SQL equivalents...
  • Making Causal Impact Analysis Easy
    The purpose of this document is to describe a robust approach to intervention analysis based on two key R packages: the CausalImpact package written by Kay Brodersen at Google and the dtw package available in CRAN...


  • Superforecasting: The Art and Science of Prediction Interesting take on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament (The Good Judgment Project) involving tens of thousands of ordinary people...
    "Superforecasting is the rare book that is both scholarly and engaging. The lessons are scientific, compelling, and enormously practical..."... 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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