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Data Science Weekly Newsletter
August 30, 2018

Editor's Picks

  • Scheduling Notebooks at Netflix
    At Netflix we’ve put substantial effort into adopting notebooks as an integrated development platform. The idea started as a discussion of what development and collaboration interfaces might look like in the future. It evolved into a strategic bet on notebooks, both as an interactive UI and as the unifying foundation of our workflow scheduler. We’ve made significant strides towards this over the past year, and we’re currently in the process of migrating all 10,000 of the scheduled jobs running on the Netflix Data Platform to use notebook-based execution...
  • Synesthesia: The Sound of Style
    Imagine a color-blind Stitch Fix Client. She opens a Fix full of colorful fabrics carefully selected by her personal stylist. How can we enrich her experience so it is not restricted to any perceived range of colors? Borrowing from the ‘eyeborg’ idea, can we present her with a personalized song along with her Fix, that will represent and enhance what her stylist chose just for her? We want to find ways for our Client to experience Synesthesia...

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Data Science Articles & Videos

  • Databook: Turning Big Data into Knowledge with Metadata at Uber
    Big data by itself, though, isn’t enough to leverage insights; to be used efficiently and effectively, data at Uber scale requires context to make business decisions and derive insights. To provide further insight, we built Databook, Uber’s in-house platform that surfaces and manages metadata about the internal locations and owners of certain datasets, allowing us to turn data into knowledge...
  • Scanning the Internet for ROS: A View of Security in Robotics Research
    Because robots can directly perceive and affect the physical world, security issues take on particular importance. In this paper, we describe the results of our work on scanning the entire IPv4 address space of the Internet for instances of the Robot Operating System (ROS), a widely used robotics platform for research. Our results identified that a number of hosts supporting ROS are exposed to the public Internet, thereby allowing anyone to access robotic sensors and actuators...
  • Scaling Uber’s Customer Support Ticket Assistant (COTA) System with Deep Learning
    Earlier this year, we introduced Uber’s Customer Obsession Ticket Assistant (COTA) system, a tool that leverages machine learning and natural language processing (NLP) techniques to recommend support ticket responses (Contact Type and Reply) to customer support agents, with Contact Type being the issue category that the ticket is assigned to and Reply the template agents use to respond. After integrating it into our Customer Support Platform, COTA v1 reduced English-language ticket resolution times by over 10 percent while delivering service with similar or higher levels of customer satisfaction...
  • Detecting Phishing With Computer Vision: Part 1, Blazar
    At Endgame, we’re constantly pushing boundaries and developing new tools and techniques to solve security problems. We have stayed on top of the range of new applications of computer vision, but saw that it was underused in the information security space. In this and a subsequent blog we will demonstrate how computer vision can be applied to the phishing challenge, including an introduction of the two approaches which we presented at BSidesLV: 1) Blazar: URL spoofing detection, and the focus of this first post...
  • Reproducible Data Analysis in Jupyter
    In light of recent discussions, here's a series of videos I made a while ago that shows my approach to reproducible data analysis in the Jupyter notebook...


  • Data Scientist - Riot Games - Los Angeles
    Riot Games was established in 2006 by entrepreneurial gamers who believe that player-focused game development can result in great games. In 2009, Riot released its debut title League of Legends to critical and player acclaim. As the most played PC game in the world, over 100 million play every month. Players form the foundation of our community and it’s for them that we continue to evolve and improve the League of Legends experience.
    As Data Scientist, you'll develop advanced machine learning algorithms and statistical models to solve critical problems and help deliver awesome player experiences. You'll partner with product teams to implement data science models into live production systems. You'll bring fresh perspective to inform decision-making toward better player experience by translating player voice into insights using your top-notch modeling and analytic skills...

Training & Resources

  • Convert List To PyTorch Tensor
    Learn how to use the PyTorch Tensor operation (torch.tensor) to convert a Python list object into a PyTorch Tensor, via a screencast video and full tutorial transcript...


  • Bayes Theorem: A Visual Introduction For Beginners
    "This book takes what can be a daunting and complex subject and breaks it down with a series of easy to follow examples which buildup to deliver a great overall explanation of how to use Bayes Theorem for basic analysis and even off-the-cuff critical thinking"...

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page

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