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Data Science Weekly Newsletter
Issue
63
February 5, 2015

Editor's Picks

  • Visual Mapping of Twitch and Our Communities, ‘Cause Science!
    Twitch has grown so quickly this year that it’s hard to keep track of all the amazing subgroups and communities that call Twitch home. To illustrate this, our Science team has recently been building visual maps of the Twitch world and we’re thrilled to share them with you!...
  • Genetic Algorithm Walkers
    What the hell is this? This observational pastime hopes to evolve walking creatures through genetic algorithms...



Data Science Articles & Videos

  • Surviving Data Science at the "Speed of Hype"
    There is this idea endemic to the marketing of data science that big data analysis can happen quickly, supporting an innovative and rapidly changing company. But in my experience and in the experience of many of the analysts I know, this marketing idea bears little resemblance to reality...
  • The Man Who Knows Whether Any Startup Will Live or Die
    Thomas Thurston thinks data science could remove a fair amount of the risk [of starting a business]. For the past nine years, he’s been honing techniques for evaluating business plans statistically rather than intuitively...
  • Quantity Versus Quality In Your Data Science Portfolio
    A prospective employer will look at your online profiles. This will help them get a bigger picture of you than what was in your resume and profile you filled out for your data science job application. When they find your data science portfolio they will judge your work. Which brings up a very important question - should you focus on quality or quantity?...
  • A Hierarchical Bayesian Drive-Survival Model of the NFL
    In this post, I describe a model of the football drive as a piecewise exponential competing risks survival model. I then fit an example implementation, embedding the drive model within a Hierarchical Bayesian model of the NFL...
  • The idea maze for AI startups
    An “idea maze” is a map of all the key decisions and tradeoffs that startups in a given space need to make. I [Chris Dixon] thought it would be interesting to show an example of an idea maze for an area that I’m interested in: AI startups. Here’s a sketch of the maze. I explain each step in detail below...



Jobs

  • Data Scientist - Contently - NYC
    Contently is on a mission to change the way content marketing is traditionally done. That means not only building a powerful technology but also leveraging a powerful network of creative experts that traditional marketers need to stay fresh and original. We are in search of a Data Scientist with a passion for discovering insight through data inference and exploration. Someone who geeks out over data and a strong understanding of statistics and machine learning combined with experience processing and generating large data sets. Working at Contently means that you will be collaborating with extremely intelligent, creative, and diverse problem-solvers who love a good story and many laughs...


Training & Resources


Books



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