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Data Science Weekly Newsletter
April 21, 2016

Editor's Picks

  • Building a (semi) Autonomous Drone with Python
    They might not be delivering our mail (or our burritos) yet, but drones are now simple, small, and affordable enough that they can be considered a toy. In this post, I'll show you how you can use Python and node.js to build a drone that moves all by itself...
  • Where Will Your Country Stand in World War III?
    In this chapter, we use a network graph technique called Social Network Analysis (SNA) to map weapons transfer between countries. By analyzing bilateral weapons trade, a network of multilateral ties can be distilled, providing insights into the complex arena of international politics...

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

  • Pride and Prejudice and Z-scores
    You might think literary criticism is no place for statistical analysis, but given digital versions of the text you can, for example, use sentiment analysis to infer the dramatic arc of an Oscar Wilde novel. Now you can apply similar techniques to the works of Jane Austen thanks to Julia Silge's R package janeaustenr (available on CRAN)...
  • Guest Post (Part I): Demystifying Deep Reinforcement Learning
    Still, while deep models for supervised and unsupervised learning have seen widespread adoption in the community, deep reinforcement learning has remained a bit of a mystery. In this blog post I will be trying to demystify this technique and understand the rationale behind it. The intended audience is someone who already has background in machine learning and possibly in neural networks, but hasn’t had time to delve into reinforcement learning yet...
  • Machine Learning Meets Economics, Part 2
    In this article I show that even when a computer can perform a task more economically than a human, careful analysis suggests that humans and computers working together can sometimes yield even better business outcomes than simply replacing one with the other....
  • Every shot Kobe Bryant ever took. All 30,699 of them
    Kobe Bryant's 30,699th and final field goal came from 19 feet with 31 seconds left against the Utah Jazz. During his 20 years with the Lakers, he fired up more than 30,000 shots, including the regular season and playoffs. Take a tour of key shots over his 20-year career, or explore the makes and misses over his long career on your own...
  • Thought Experiments in the Browser
    In some cases, the best aid to decision-making is less about finding “the answer” in the data and more about developing a deeper understanding of the underlying problem. In this post we will focus another tool that is often overlooked: interactive simulations through the means of agent based modeling...
  • Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
    In this paper we study the problem of image representation learning without human annotation. Following the principles of self-supervision, we build a convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles as a pretext task, which requires no manual labeling, and then later repurposed to solve object classification and detection...


  • Senior Data Scientist - Warby Parker - New York, NY
    Warby Parker is looking for a passionate senior data scientist to join our Technology team, helping build the next great lifestyle brand. With dozens of physical retail locations, a thriving e-commerce site, and a global supply chain, the Data Science team tackles a variety of exciting problems and domains. Working with lines of business owners and analysts, we have a direct, meaningful impact on the company. However, we know that we can do even more, so we’re searching for someone special to help grow our small (but mighty!) team...

Training & Resources

  • Stat212b: Topics Course on Deep Learning
    This topics course aims to present the mathematical, statistical and computational challenges of building stable representations for high-dimensional data, such as images, text and data. We will delve into selected topics of Deep Learning, discussing recent models from both supervised and unsupervised learning. Special emphasis will be on convolutional architectures, invariance learning, unsupervised learning and non-convex optimization...


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