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Data Sets for Data Science

by Joseph Rickert Recently, I had the opportunity to be a member of a job panel for Mathematics, Economics and Statistics students at my alma mater, CSUEB (California State University East Bay). In the context of preparing for a career in data science a student at the event asked: “Where can I find good data sets?”. This triggered a number of thoughts: the first being that it was time to update the list of data sets that I maintain and blog about from time to time. So, thanks to that reminder I have added a few new links to the page, including a new section called Data Science Practice that links to some of the data sets used as examples in Doing Data Science by Rachel Schutt and Cathy O’Neil.  Additionally, I have provided a direct link to the BigData Tag on infochimps and pointed out that multiple song data sets are available. However, to do justice to student’s question it is necessary to give some thought to exactly what a “good” practice data set might look like. Here are three characteristics that I think a practice data set should have to be good: It should be big enough to pose some computational challenges without being so big that it requires a cluster or some specialized hardware just to get started. It should require some cleaning or pre-processing (making decisions about mission data for example) but not appear to be hopelessly corrupt; dirty but not to dirty. It should be rich enough that once you have gone through the trouble of accessing and cleaning it there are enough variables or features to suggest multiple questions to analyze, or make it possible to try out different machine learning algorithms. Here are three data sets that meet these criteria in ascending order of degree of difficulty: The first suggestion is the MovieLens data set which contains a million ratings applied to over 10,000 movies by more than 71,000 users. The download comes in two sizes, the full set, and a 100K subset. Both versions require working with multiple files. Near the top of anybody’s list of practice data sets, and second on my little list because of degree of difficulty is the airlines data set from the 2009 ASA challenge. This data set which contains the arrival and departure information for all domestic flights from 1987 to 2008 has become the “iris” data set for Big Data. With over 123M rows it is too big to it into your laptop’s memory and with 29 variables of different types it is rich enough to suggest several analyses. Moreover, although the version of the data set maintained on the ASA website is fixed and therefore perfect for benchmarking, the Research and Innovative Technology Administration Bureau of Transportation Statistics continues to add to the data on a monthly basis. Go to RITA to get all of the data collected since the ASA competition ended.    Last on my short list is the Million Song data set. This contains features and meta data for one million songs which were originally provided by the music intelligence company Echo Nest. The data is in the specialized HDF5 format which makes it somewhat of a challenge to access. The data set maintainers do provide wrapper functions to facilitate downloading the data and avoiding some of the complexities of the HDF5 format. However, there are no R wrappers! The last I checked, the maintainers had a paragraph about there being a problem with their code along with an invitation for R experts to contact them (This would clearly be for extra points.) For more details about the contents of the data set look here. As a final note, it is much easier use R to analyze the Public Data Sets available through Amazon Web Services now that you can run Revolution R Enterprise in the Amazon Cloud. We hope to have more to say about exactly how to go about doing this in a future post. However, everything you need to get started is in place including a 14 day free trial (Amazon charges apply) for Revolution R Enterprise. All you need is your own Amazon account. Please let me know if you have additional links to useful, publically available data sets that I have missed. We very much appreciate the contributions blog readers have made to the list of data sets.  

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

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