Data Science Weekly Newsletter - Issue 148

Issue #148

Sept 22 2016

Editor Picks
  • What Math Looks Like in the Mind
    In a surprise to scientists, it appears blind people process numbers by tapping into a part of their brains that’s reserved for images in sighted individuals...
  • Is Artificial Intelligence Permanently Inscrutable?
    As exciting as their performance gains have been, though, there’s a troubling fact about modern neural networks: Nobody knows quite how they work. And that means no one can predict when they might fail...
  • Battle of the Data Science Venn Diagrams
    Personally, I've recently decided to avoid the controversy by calling myself a data spelunker. (Data miners are out of vogue anyway.). As a field in search of a definition, it's unsurprising that you can find a lot of different attempts to define it. As a field full of data nerds with a penchant for visualization, it's also unsurprising that a lot of them use Venn diagrams...

A Message from this week's Sponsor:


  • Data Science in Practice: Common Applications of Data Science with Concrete, Real-Life Use Cases via @YhatHQ

    In this whitepaper we provide concrete examples of how data science is applied in reality. You'll learn how real companies are using machine learning to vastly improve their products and operations.

Data Science Articles & Videos

  • Where will Artificial Intelligence come from?
    As artificial intelligence continues to make progress, I would like to ask the following question: Where will the next major advance towards general purpose artificial intelligence come from? Below I list seven possible areas which I believe could be the answer to this question...
  • Analyzing the conditions for studying stars
    For astronomers it is extremely useful to know the current observing conditions, not least because prime observing time is precious and optimal scheduling is critical. At the Paranal Observatory, home to the Very Large Telescope (VLT), conditions are recorded every minute during regular operations. We will explore the historical observing conditions at the VLT over the past 16 years and answer a lingering question in the minds of telescope operators: What is the probability that good conditions will last?...
  • Google's New Vacation App Was 280 Years In The Making
    Kaliningrad is a Russian seaport named for a Soviet revolutionary. It sits near the Baltic Sea, between Poland and Lithuania, and it’s a place where pre-Putin Russian leaders would occasionally threaten to install nuclear missiles. But in the 18th century, it was a city called Königsberg in the German kingdom of Prussia. And it was a math problem...
  • NEW TECH and A Little Story About Neymar, Andros, and Eden Hazard
    Today, I wanted to talk a little more about what I learned regarding player evaluation while going from zero knowledge in 2013 to running worldwide recruitment for two clubs in 2015. As part of that, I’ll introduce the new attacker radars in print for the first time, and I’ll talk about three of the most famous players in the world: Neymar, Eden Hazard, and… Andros Townsend?!?...
  • Playing FPS Games with Deep Reinforcement Learning
    Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. In this paper, we present the first architecture to tackle 3D environments in first-person shooter games, that involve partially observable states...
  • Improving Brand Analytics with an Image Logo Detection Convolutional Neural Net in TensorFlow
    For my final Metis project, I developed an application that can improve brand analytics through logo detection in images. The core of my solution leverages a Deep Convolutional Neural Network developed and trained using Google’s Deep Learning library, TensorFlow. Since my presentation was constrained to only four minutes, I’ll use this post to elaborate on the slides I presented (provided as screenshots throughout this post). My hope is this post will be useful to you if you’re trying to build your own image detection model or just trying to understand more about deep learning!...


  • Data Scientist, Product Analytics - Snapchat - Los Angeles, CA

    Over 150 million people use Snapchat every day to Snap with family, watch Stories from friends, see events from around the world, and explore expertly-curated content from top publishers. In short, we are a passionate team working hard to build the best platform in the world for communication and storytelling.

    We’re looking for a Data Scientist to join Team Snapchat! Working closely alongside the Growth, Revenue, and Analytics teams, you will be tasked with creating inventive, data-based approaches to solving difficult business problems. Working from our Venice, CA headquarters, you’ll collaborate with the Product Marketing team to transform business questions into data analysis.

Training & Resources

  • Markov Chain Monte Carlo Without all the Bullshit
    I have a little secret: I don’t like the terminology, notation, and style of writing in statistics. I find it unnecessarily complicated. So to counter, here’s my own explanation of Markov Chain Monte Carlo...
  • Illustrated Guide to ROC and AUC
    In a past job interview I failed at explaining how to calculate and interprete ROC curves – so here goes my attempt to fill this knowledge gap...



  • Test-Driven Machine Learning

    The book begins with an introduction to test-driven machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression...

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details :) - All the best, Hannah & Sebastian
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