Data Science Weekly Newsletter

Issue

December 12, 2013

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Editor's Picks

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This Data Scientist Spent A Year Deep Inside The New York Times. Here’s What He Discovered
Brian Abelson spent the last year at The Times using data and analytics to understand Times content. Abelson had access to one of the most coveted datasets in publishing, The New York Times’ web and social traffic...we talk to Abelson about his year at The Times, as he attempted to create a better set of metrics focused on measurements of human response to media, like impact and behavioral change...

In The Hospital Of The Future, Big Data Is One Of Your Doctors
From our genomes to Jawbones, the amount of data about health is exploding. Bringing in top Silicon Valley talent, one NYC hospital is preparing for a future where it can analyze and predict its patients' health needs--and maybe change our understanding of disease...

Foursquare: How We Built Our Model Training Engine
At Foursquare, we have large-scale machine-learning problems. From choosing which venue a user is trying to check in at based on a noisy GPS signal, to serving personalized recommendations, discounts, and promoted updates to users based on where they or their friends have been, almost every aspect of the app uses machine-learning in some way. We’ve been building out a Model Training Engine (MTE) to automate our (machine) learning from user data. Here’s an overview to whet your appetite....

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You Might Be A Data Scientist If...
As I meet up-and-coming data scientists, I've realized that we share a surprising number of very specific experiences. Here's a list of things of these data science rites of passage, in no particular order...

Can Machines Learn To Predict A Violent Conflict?
As part of IPI’s new Data Lab project, we have been looking at ways to leverage data science methods into our policy research on peace, security, and conflict prevention. One area of research over the last year has been to research the application of machine learning specifically to the conflict prevention and early warning problems. The first stage focused on two main aspects: feasibility and added value...

Machine Learning Netflix Style With Xavier Amatriain
Xavier Amatriain (PhD) is Director of Algorithms Engineering at Netflix. He leads a team of researchers and engineers designing the next wave of machine learning approaches to power the Netflix product. He is working on the cross-roads of machine learning research, large-scale software engineering, and product innovation...

Making Clinical Data Analytics Count
Within healthcare facilities but outside of the healthcare information technology clique, the concept of data analytics seems to be the trendiest buzzword to capture the ears and eyes of non-IT administrators and clinicians alike. Yet questions linger about what this concept truly means, whether we’re collecting too much, using too little and wasting time and money in the process. Health Management Technology reached out to group of executives in the data analytics space to clear up some of the fog...

BayesDB - Query The Probable Implications Of Your Data
BayesDB, a Bayesian database table, lets users query the probable implications of their data as easily as a SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with no statistics training can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries...

Scoring Dynamics Across Professional Team Sports: Tempo, Balance And Predictability
Despite growing interest in quantifying and modeling the scoring dynamics within professional sports games, relative little is known about what patterns or principles, if any, cut across different sports. Using a comprehensive data set of scoring events in nearly a dozen consecutive seasons of college and professional (American) football, professional hockey, and professional basketball, we identify several common patterns in scoring dynamics...

This Scientist Uses The NYT Archive To Eerily, Accurately Predict The Future
The New York Times might be a widely respected chronicler of past events, but can we use it to divine the future? Kira Radinsky, a 27-year-old Israeli computer prodigy dubbed the “web prophet” says yes. She has written an algorithm that dissects old news stories and other Internet postings to look for past cause and effect, and then can alert us to possible disasters, geopolitical events, and disease outbreaks...

At MailChimp, Data Science Works Behind The Scenes
Data science doesn’t need to look cool. It doesn’t need to use trendy technologies, either. What it ought to do is solve problems. And data science has done that for MailChimp, in a variety of applications...

Kayak Uses Big Data To Predict The Best Day To Book Your Travel Journey
Kayak is a meta search engine for the travel industry doing what the large travel platforms, Orbitz, Expedia etc, did for the individual (airline) websites. They aggregate the aggregators and in the mean time they add new layers of information to the basics to give a rich user experience. However, for their flight search they have moved into predictive analytics...

Six Interview Vignettes for Data Scientists
The Changing Job Market for Data Scientists - Advice from Top Bosses. The topics discussed are broad – from finding the right people to work as data scientists, to the challenges data scientists face in today’s professional market...starting with well-known data scientist Hilary Mason...

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Junior Data Scientist, zulily
HQ in Seattle, WA, zulily is pioneering the change in the retail market with member-only access to the most unique and sought-after children's boutique brands! zulily is seeking an intellectually curious, collaborative data expert to work as a statistician, data miner and business analyst...

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Free Tech Course At Gilt: Big Data Analysis With SQL
If you want to see how SQL is applied to big data analytics, we have the perfect holiday present for you: A free, full-day course in advanced SQL and analytics! Taught by our very own Principal Data Scientist Igor Elbert, this eight-hour course will show you how SQL is applied in the big data field. You’ll also come away with a better understanding of what MapReduce is, and learn how to analyze click-stream data...

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