Web Picks (week of 17 September 2018)

Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.

  • Models Will Run the World
    The software revolution has transformed business. What’s next? Processes that constantly improve themselves without need of human intervention.
  • How Game Apps That Captivate Kids Have Been Collecting Their Data
    A lawsuit by New Mexico’s attorney general accuses a popular app maker, as well as online ad businesses run by Google and Twitter, of violating children’s privacy law.
  • Why data culture matters
    Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes. Here are seven principles that underpin a healthy data culture.
  • The First Notebook War
    “So Joel Grus doesn’t like Jupyter notebooks. Here are some of my thoughts on notebooks, IDE, and R Markdown.” Very interesting read.
  • A mathematical model captures the political impact of fake news
    The mathematical theory of communication—one of science’s finest achievements—provides an objective way to simulate how deliberately inaccurate reports influence voting behavior.
  • Under the Hood of Uber’s Experimentation Platform
    “Experimentation is at the core of how Uber improves the customer experience. Uber applies several experimental methodologies to use cases as diverse as testing out a new feature to enhancing our app design. Uber’s Experimentation Platform (XP) plays an important role in this process.”
  • Anatomy of an AI System
    The Amazon Echo as an anatomical map of human labor, data and planetary resources.
  • Learn Deep Reinforcement Learning in Depth in 60 days
    This repository wants to guide you through the Deep Reinforcement Learning algorithms, from the most basic ones to the highly advanced AlphaGo Zero. You will find the main topics organized by week and the resources suggested to learn them.
  • Data visualisation, from 1987 to today
    Looking back over 30 years of making charts and maps for The Economist.
  • Desperate for Data Scientists
    LinkedIn reports dramatically increasing shortage of data scientists across U.S.
  • Google Dataset Search
    An alternative for Kaggle datasets, from Google.
  • Preserving Outputs Precisely while Adaptively Rescaling Targets
    “We developed PopArt, a technique that can adapt the scale of scores in each game so the agent judges the games to be of equal learning value, no matter the scale of rewards available in each specific game.”
  • The use of Embeddings in OpenAI Five
    “In this blog post I would like to focus on one aspect of their network architecture – their inventive use of embeddings to handle a huge and variable number of policy inputs and outputs.”
  • An ethics checklist for data scientists
    “deon is a command line tool that allows you to easily add an ethics checklist to your data science projects. We support creating a new, standalone checklist file or appending a checklist to an existing analysis in many common formats.”
  • Scaling Featuretools with Dask
    How to scale automated feature engineering using parallel processing.