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Data Science Weekly Newsletter
December 22, 2016

Editor's Picks

  • A non-comprehensive list of awesome things other people did in 2016
    Like in previous years I’m making a list, again right off the top of my head. If you know of some, you should make your own list or add it to the comments! I write this post because a blog often feels like a place to complain, but we started Simply Stats as a place to be pumped up about the stuff people were doing with data...
  • 50 things I learned at NIPS 2016
    I learned many things about AI and machine learning at the NIPS 2016 conference. Here are a few that are particularly suited to being communicated in the space of a few sentences...
  • A Guide to Solving Social Problems with Machine Learning
    The mix of enthusiasm and trepidation over the potential social impact of machine learning is not unique to local government or even to government: non-profits and social entrepreneurs share it as well. The enthusiasm is well-placed. For the right type of problem, there are enormous gains to be made from using these tools. But so is the trepidation: as with all new “products,” there is potential for misuse. How can we maximize the benefits while minimizing the harm?...

A Message From This Week's Sponsor

  • Because of Dataquest I’m Finally Learning Data Science
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Data Science Articles & Videos

  • Rocket AI: 2016’s Most Notorious AI Launch and the Problem with AI Hype
    It’s 3 AM on a warm Thursday night in December, a usually quiet street in the Gothic Quarter in Barcelona is bustling with activity, as a cohort of 200 artificial intelligence researchers leave in single-file out of a sprawling yellow mansion. The police count heads as the researchers film the procession on their phones and tweet #rocketai...
  • Family Matters: Genealogy Knowledge Graphs Made Easy with GRAKN.AI
    Highly interconnected data with heterogeneous types are a precious source of knowledge, but challenging to maintain, query, analyse and understand. GRAKN.AI is an open-source knowledge graph data platform that uses the power of machine reasoning to help you overcome these challenges to build intelligent and cognitive systems...
  • Data Science at Slush 2016
    Slush, Europe’s leading startup event, took place in Helsinki from November 30th to December 1st. Thousands of attendees including startups, investors, tech companies, and researchers came together to get a glimpse of the latest developments in a massive range of fields. Our data science team went along too, and I’m going to talk about some of the trends we saw there....
  • What is the Chance of an Earthquake?
    What is the chance that an earthquake of magnitude 6.7 or greater will occur before the year 2030 in the San Francisco Bay Area? The U.S. Geological Survey estimated the chance to be 0.7 ± 0.1 (USGS, 1999). In this paper, we try to interpret such probabilities...
  • What is XGBoost and why include it in your Machine Learning toolbox
    At the Higgs Boson Data Science competition everyone’s attention was caught by XGBoost - a new classification algorithm which outperformed all other Machine Learning algorithms used in this competition and brought the 1st place to its developers. By its nature XGBoost is similar to GBM, because it’s a tree-based approach, but its flexibility, scalability, and exceptional accuracy is superior to GBM and other classification methods. Here are some of the main reasons why you should consider using XGBoost for your next classification problem...
  • Named Entity Classification
    The customers of communicate with us through different mediums. They perform queries on our search engine, provide us with reviews about their stay and describe their opinion about different destinations. All this communication creates an abundance of textual information — and it's a key part of our job to understand it. The first step towards this goal is the recognition of the named entities (the sequences of words in the text which correspond to categories such as cities, accommodation, facilities, etc.). In this blog post, we display a comparison of different approaches that can be used in order to tackle such a Named Entity Classification task...


  • Principal Data Scientist - Comcast - Philadelphia, PA
    Are you passionate about the future of data analytics and desire to help shape it? If so, Comcast is looking for a Data Scientist with deep quantitative and statistical skills to join a Data Science team which consists of world class business minds and scientists tasked with driving transformational change through evidence-based decision making.
    Ideal candidate for the role would possess advanced predictive machine learning skills, have expert level background in Spark, Scala, Python, R or SAS with strong SQL skills. Excellent written, verbal and presentation skills are needed along with top-tier communication and listening skills. Preference given for candidates with experience in digital analytics - site optimization, marketing-mix, digital channel attribution, end-to-end digital campaign experience. Experience collecting and integrating of social, mobile and site qualitative and quantitative data to highlight important themes in consumer engagement, customer journeys across desktop/mobile site experiences and mobile app...

Training & Resources


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