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Data Science Weekly Newsletter
April 30, 2015

Editor's Picks

  • A Statistical Analysis of the Work of Bob Ross
    In total, Ross painted 381 works on the show, relying on a distinct set of elements, scenes and themes, and thereby providing thousands of data points. I decided to use that data to teach something myself: the important statistical concepts of conditional probability and clustering, as well as a lesson on the limitations of data...

Data Science Articles & Videos

  • "People Who Like This Also Like ... "
    A while ago a friend of mine asked me how I would go about building a 'People Who Like This Also Like ...' feature for a music startup he was working at. For each band or musician, he wanted to display a list of other artists that people might also be interested in...
  • When is Cheryl's Birthday?
    Cheryl's puzzle was designed to be solved with a pencil, the greatest problem-solving tool in the history of mathematics (although some prefer a pen, chalk, marker, or a stick for drawing in the sand). But I will show how to solve it with another tool: computer code...
  • Amazon Machine Learning: use cases and a real example in Python
    Here I would like to share my personal experience with this amazing technology, introduce some of the most important – and sometimes misleading – concepts of machine learning, and give this new AWS service a try with an open dataset in order to train and use a real-world AWS Machine Learning model...
  • Engineering the Hiring Process
    At Karat, we are passionate about improving the effectiveness and efficiency of hiring. From time to time, we’ll post articles from our employees, advisors, and friends so they can share what they’ve learned from their personal hiring experiences. Daniel Tunkelang is an advisor to Karat. He’s worked at LinkedIn, Google, and Endeca in a variety of technical leadership roles, specializing in relevance engineering and data science...


  • Research Scientist - Fitbit - San Francisco, CA
    Fitbit is the leader in the explosive market of health and fitness wearables. We empower and inspire our users to lead healthier and more active lifestyles with simple and delightful products. We are building a world-class research team of hacker-scientist-types to dream up, prototype, and deliver shipping products. Research at Fitbit spans a big set of problems from hardware development to embedded signal processing algorithms to data mining, all with a twist of experimentation. We work in a dynamic and collaborative environment where the goal is to learn things quickly, iterate fast, and make awesome products...

Training & Resources

  • Rodeo: A data science IDE for Python
    Today we're excited to introduce a new project: Rodeo. Rodeo is an IDE that's built expressly for doing data science in Python. Think of it as a light weight alternative to the IPython Notebook...
  • Awesome-R: A curated list of the best add-ons for R
    Qin Wenfeng has taken the trouble to curate the best add-ons to R in their list, awesome-R: A curated list of awesome R frameworks, packages and software. The list provides several (but not too many!) recommendations for R users in the areas of IDEs, data manipulation packages, database integration frameworks, machine learning suites, R-related websites, and much more...
  • Soren Macbeth - Data Science in Clojure
    Yieldbot's data platform is built on top of clojure. I will cover some of our experience using clojure for data (sciencing and platform). I will cover our two major open source projects, marceline, a clojure dsl for apache storm/trident, and flambo, a clojure dsl for apache spark...


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