Digging Into Data: Spring 2014


Jordan Boyd-Graber


iSchool at Maryland


Spring 2014


Computers have made it possible, even easy, to collect vast amounts of data from a wide variety of sources. It is not always clear, however, how to use those data and how to extract useful information from data. This problem is faced in a tremendous range of scholarly, government, business, medical, and scientific applications. The purpose of this course is to teach some of the best and most general approaches to get the most out of data through clustering, classification, and regression techniques. Students will gain experience analyzing several kinds of data, including document collections, financial data, scientific data, and natural images.

Required Textbook:

Williams, G. 2011. Data Mining with Rattle and R.

Link to Syllabus:


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