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Data Science Weekly Newsletter
October 6, 2016

Editor's Picks

  • Deep-Fried Data
    Today I'm here to talk to you about machine learning. I'd rather you hear about it from me than from your friends at school, or out on the street...Machine learning is like a deep-fat fryer. If you’ve never deep-fried something before, you think to yourself: "This is amazing! I bet this would work on anything!”...And in any deep frying situation, a good question to ask is: what is this stuff being fried in?...
  • Keynote Session: Dr. Edward Tufte - The Future of Data Analysis
    Data analysis seeks to learn from experience.  Better inferences require better thinking and better tools. Practical advice about how to make more credible conclusions based on data. What we can expect in the future, and what we should aspire to in the future...

A Message From This Week's Sponsor

  • Harness the business power of big data.

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Data Science Articles & Videos

  • The Simpsons by the Data
    Analysis of 27 seasons of Simpsons data reveals the show’s most significant side characters, a pattern of patriarchy, declining TV ratings, and more...
  • Automatically Grading Multiple Choice Exams From Photos With Python
    Over the past few months I’ve gotten quite the number of requests landing in my inbox to build a bubble sheet/Scantron-like test reader using computer vision and image processing techniques... So here is a bubble sheet multiple choice scanner and test grader using OMR, Python and OpenCV...
  • Perceptual Losses for Real-Time Style Transfer and Super-Resolution
    fast-neural-style: The paper builds on A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge by training feedforward neural networks that apply artistic styles to images. After training, our feedforward networks can stylize images hundreds of times faster than the optimization-based method presented by Gatys et al...
  • Multiple Narrative Disentanglement: Unraveling Infinite Jest
    Many works (of both fiction and non-fiction) span multiple, intersecting narratives, each of which constitutes a story in its own right. In this work I introduce the task of multiple narrative disentanglement (MND), in which the aim is to tease these narratives apart by assigning passages from a text to the sub-narratives to which they belong. The motivating example I use is David Foster Wallace’s fictional text Infinite Jest...
  • Replication in Data Science - A Dance Between Data Science & Machine Learning
    We use Iterative Supervised Clustering as a simple building block for exploring Pinterest's Content. But simplicity can unlock great power and with this building block we show the shocking result of how hard it is to replicated data science conclusions. This begs us to challenge the future for When is Data Science a House of Cards?...
  • #TrumpWon? trend vs. reality. A deep dive into the underlying data
    Why is everyone so obsessed with this hashtag and the fact that it was in Twitter’s trending topics list the morning after the first presidential debate? Perhaps the competitive nature of a presidential debate — the fact that there’s supposed to be a “winner” — means that we’re reading into any available data point. Maybe due to the nature of this specific election cycle, where facts seem to have become subjective, as people in online echo-chambers consume what they want to believe...


  • Data Scientist, Growth - Coursera - Mountain View, CA
    Coursera is scaling a global platform to provide universal access to the world’s best education, and we’re driven by the passion and mission to let people learn without limits. We use data to drive our products and our business, and to better serve our learners.
    We’re looking for a talented, creative, and driven data scientist with a sharp eye for UX design, strong algorithmic and analytic skills, and an interest in expanding the reach and quality of online education by improving our discovery experiences. Our ideal candidate is an independent, analytically-minded individual with strong software engineering and statistical modeling skills, who shares our passion for education. In this role, you’ll be directly involved in the development, implementation, and evaluation of discovery products, including search and personalized recommendations...

Training & Resources

  • Hadoop architectural overview
    In this post, we’ll explore each of the technologies that make up a typical Hadoop deployment, and see how they all fit together...
  • An Introduction to Machine Learning in Julia
    In this post, we introduce a simple machine learning algorithm called K Nearest Neighbors, and demonstrate certain Julia features that allow for its easy and efficient implementation. We will demonstrate that the code we write is inherently generic, and show the use of the same code to run on GPUs via the ArrayFire package...


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