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The most recent edition of the Revolution Newsletter is out. The news section is below, and you can read the full February edition (with highlights from this blog and community events) online. You can subscribe to the Revolution Newsletter to get it monthly via email.

I can barely get my Ar.Drone to land on the box it came on (I am not a good drone pilot), but these quadrocopters from can frickin' juggle (with thanks to Donnie Berkholz):


A new book by Jeffrey Stanton from Syracuse Iniversity School of Information Studies, An Introduction to Data Science, is now available for free download.

Had a great time earlier this week on a Google Hangout as part of the IBM Opinionated Infrastructure series. Moderator James Governor (analyst from RedMonk) kept the conversation lively, with topics ranging from to the value of information to the benefits of predictive analytics and evolution of Hadoop. R gets a mention at several points in the conversation, which you can catch in the replay embedded below.


Here are three recent news articles that feature interviews with members of the Revolution Analytics team talking about the importance of the R language:

This guest post is by Tammer Kamel, Founder of Quandl

Finding and formatting numerical data for analysis in R or Excel or indeed any application is a pain that all real world data analysts know all too well.  In aggregate I have probably spent weeks of my life trying to find data on the web.  And several more weeks validating, formatting and cleaning the data.  Analysis offers data scientists interesting, intellectually stimulating problems.  But data acquisition, the necessary precursor, offers only tedium and pain.  It's a time vampire.

I'll be on a Google Hangout in a couple of hours to join a discussion on big data, analytics, and expert integrated systems. The conversation will be led by RedMonk's James Governor (@monkchips), and I'll be joining a panel of experts on big data and analytics:

Anyone interested in playing around with the data generated by the PITCHf/x cameras at major league baseball games should definitely check out the pitchRx package from Carson Sievert. Major League Baseball Advanced Media makes the data available for download, and this package provides an interface from R to the speed, position and pitcher data for just about every

The yhat blog lists 10 R packages they wish they'd known about earlier. Drew Conway calls them "10 reasons to always start your analysis in R". They're all very useful R packages that every data scientist should be aware of. They are: