Data Science Weekly Newsletter - Issue 5

Issue #5

December 26 2013

Editor Picks

 
  • What I Learnt From 2 years Of 'Data Sciencing'

    Last week was my last day at uSwitch.com. From becoming aware of data scientist as a valid job title on my job offer letter, to speaking at Strata London, to signing a book deal to write about it in our book on Web Data Mining (that's progressing at a glacial pace), I figured that I should jot down some takeaway lessons while this experience is still fresh...
  • Intelligent Probabilistic Systems: Ryan Adams (Harvard Prof) Interview

    We recently caught up with Ryan Adams - Assistant Professor of Computer Science at the Harvard School of Engineering and Applied Sciences and leader of the HIPS (Harvard Intelligent Probabilistic Systems) group - to learn more about the research underway at HIPS and his recent work putting powerful probabilistic reasoning algorithms in the hands of bioengineers...
  • Teaching A Computer To Read: NLP Hacking In Python

    Scripted recently released a new feature called Experts, which allows us to efficiently and confidently group together expert writers in a given subject...Part of what determines whether a writer is a good fit for an Expert team is knowing how many pieces they’ve written about that team’s subject matter. This entailed an interesting machine learning problem...
 
 

Data Science Articles & Videos

 
  • This Algorithm Can Make Pictures Of Your Face More Memorable
    A team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory recently developed an algorithm that can make photos of faces easier to recall. The algorithm makes slight tweaks to the size, shape, and appearance of the face; then, almost like magic, people remember the adjusted photo of the person more often. This is not pseudoscience or sorcery. It's math...
  • How LinkedIn Is Using Data To Help You Find The Right Career
    At VentureBeat’s DataBeat/Data Science Summit in November, I caught up with LinkedIn’s head of data science, Jim Baer, to chat about how the company is making use of its vast store of information it’s collecting about recruiting and careers...
  • Machine Learning Used To Create An HIV Vaccine
    When we think of machine learning it’s usually in the context of robotics—giving an algorithm a large set of input data in order to train it for a certain task like navigation or understanding your handwriting. But it turns out you can also train a nasty virus to go to sleep and never wake up again. That’s exactly what the Immunity Project has been doing...
  • Detecting Outlier Car Prices On The Web
    We're pleased to bring you this post, courtesy of Josh Levy, Director of Data Science at Vast.com. Based in Austin, TX, Vast is a leading provider of data and technology powering vertical search for automotive, travel and real estate. This is a post exploring a real world outlier detection problem and the approach he took to solving it at Vast...
  • Probabilistic Data Structures For Web Analytics And Data Mining
    In this article, I provide an overview of probabilistic data structures that allow one to estimate advanced metrics and trade precision of the estimations for the memory consumption. These data structures can be used both as temporary data accumulators in query processing procedures and, perhaps more important, as a compact – sometimes astonishingly compact – replacement of raw data in stream-based computing...
  • A Roadmap For Rich Scientific Data Structures In Python
    So, this post is a bit of a brain dump on rich data structures in Python and what needs to happen in the very near future. I care about them for statistical computing and financial data analysis. Other people in the scientific Python community want them for numerous other applications: geophysics, neuroscience, etc. It’s really hard to make everyone happy with a single solution. But the current state of affairs has me rather anxious. And I’d like to explain why...
  • Deep Neural Network Learns Language From Wikipedia
    Big news for big data: the makers of Ersatz, a platform for building “deep neural networks” in the cloud, have fed their algorithm over 4 million Wikipedia articles, and this word cloud is what it learned...
  • Natural Language Processing In The Kitchen
    Natural Language Processing is a field that covers computer understanding and manipulation of human language, and its ripe with possibilities for news gathering. You usually hear about it in the context of analyzing large pools of legislation or other document sets, attempting to discover patterns or root out corruption. I decided to take it into the kitchen for my latest project: The Times California Cookbook recipe database...
  • Netflix Open Sources Its Data Traffic Cop, Suro
    Netflix has open sourced a tool called Suro that collects event data from disparate application servers before sending them to other data platforms such as Hadoop and Elasticsearch. It is more big data innovation that hopefully finds its way into the mainstream...
 
 

Jobs

 
  • Chief Data Scientist, Lazada, Singapore or Bangkok

    Lazada operates in South East Asia with Amazon's business model (the everything store). We have millions of customers and raised a total of a couple hundred million USD to grow further and faster. Another company in our investors' portfolio has seen enormous success creating a data science team in Haskell, so we are in the process of building our own. The first hire will be the most important and this is what this ad is about: coming in to build and lead the team...
 
 

Training & Resources

 
  • The Field Guide To Data Science

    Booz Allen Hamilton created The Field Guide to Data Science to help organizations of all types and missions understand how to make use of data as a resource. The text spells out what Data Science is and why it matters to organizations as well as how to create Data Science teams...
  • Online Learning Curriculum For Data Scientists

    “Is there any online reading or courses I can do to get into data analysis?”... I get asked this question a lot in the workplace. In this post I propose a learning path to “get into data analysis”...
 
 
P.S. Are you a Data Scientist? Would you like to share your story/work with the community? If so, we would love to interview you for our blog ... please just email us at team@datascienceweekly.org. Looking forward to hearing from you :)
 
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