Dear Colleagues

I am offering a course next semester on “data/election sciences”, co-taught with a statistics professor.  This is a fairly big experiment at Reed College, which does not have a history of cross-listed courses and where computer science and statistics have only recently been a serious part of our curriculum.  In short, new, innovative, scary, fun!

Wondering if anyone can provide links or send me examples of data science, big data, or election sciences syllabi, graduate or undergraduate level.  This will be an undergraduate class, obviously.  If it helps, students will have to have taken at least one semester of statistics in the Math department, one semester of introductory political science and one upper division political science or social science.  Most will have moderate level of R skills and the expectation is that we’ll use R in the course.

The overall pitch is to combine insights from statistics and political science to take on data processing and inferential problems associated with using very large voter registration and voter history files, and if possible, combining these files with commercial data bases, possibly geocodes, and attacking problems  like inferring race (when not on the voter file), inferring individual level support or opposition to ballot measures, identifying pockets of non-voters, etc.  That’s just a brainstorm of possibilities.  

Thanks in advance!

---
Paul Gronke
Professor, Reed College
Director, Early Voting Information Center
http://earlyvoting.net

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Media Inquiries: Kevin Myers [log in to unmask]

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