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Data Science Weekly Newsletter
July 14, 2016

Editor's Picks

A Message From This Week's Sponsor

  • Where science and policy change the world. And You.

    Apply your knowledge & skills to federal policy via the AAAS Science & Technology Policy Fellowships. A year-long professional development opportunity for doctoral level data scientists to serve in the federal government in Washington, D.C.
    STPF fosters a career-enhancing network of science leaders who understand policymaking & contribute to society...

Data Science Articles & Videos

  • What's Being Built With Deep Learning?
    Applications built using deep learning seems to be straight out of science fiction. Here is a partial sample of some of the incredible applications that have been developed so far...
  • Meet the data scientist plotting Twitter’s revival
    Data science influences decisions large and small at Twitter. Aside from the new team, Twitter has roughly 30 data scientists – out of 3,800 employees – sitting in San Francisco. “We are a small team. But by understanding our users, we can change the course of the company,” Linus tells me...
  • Home Depot Product Search Relevance, Winners' Interview
    A total of 2,552 players on over 2,000 teams participated in the Home Depot Product Search Relevance competition which ran on Kaggle from January to April 2016. Kagglers were challenged to predict the relevance between pairs of real customer queries and products. In this interview, the first place team describes their winning approach and how computing query centroids helped their solution overcome misspelled and ambiguous search terms...
  • Thinking in SQL vs Thinking in Python
    Over the years, I’ve used a variety of languages and tools to analyze data. As I think back on my time using each tool, I’ve come to realize that each encourages a different mental framework for solving analytical problems. Being conscious of these frameworks—and the behaviors they promote—can be just as important as mastering the technical features of a new language or tool...
  • The Theorem Every Data Scientist Should Know (Part 2)
    Last week, I wrote a post about the Central Limit Theorem. In that post, I explained through examples what the theorem is and why it’s so important when working with data. If you haven’t read it yet, go do it now. To keep the post short and focused, I didn’t go into many details. The goal of that post was to communicate the general concept of the theorem. In the days following it’s publication, I received many messages. People wanted me to go into more details.Today, I’ll dive into more specifics...


  • Data Scientist - Hulu - Santa Monica, CA
    Data Science Team at Hulu combines deep data analysis and research of our rich user data to present a compelling vision around user retention and preferences across the vast ecosystem of Hulu’s product offerings and content. We are looking for data scientists who are passionate about using data to drive strategy and product recommendations. You will be engaged with senior leaders to design well-constructed analyses and work cross-functionally with analysts, product managers and engineers to effectively deliver actionable results. You will work on variety of domains such as data science, machine learning and optimization; lead cutting edge analytical solution development pipeline and contribute to external research via attending conferences and collaborations...

Training & Resources

  • How to Start Learning Deep Learning
    Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it...
  • Altair
    Declarative statistical visualization library for Python. Altair's API is simple, friendly and consistent and built on top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code...


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