This course encourages freshmen to begin thinking critically about what constitutes numerical evidence and how to display that information visually. The course uses the R programming language and environment. Through interesting and intuitive examples, statistical topics like clustering and out-of-sample prediction are introduced, as well as visualization topics like animation.
In this course, we consider ways to illustrate compelling stories hidden in a blizzard of data. Equal parts art, programming, and statistical reasoning, data visualization is a critical tool for anyone doing analysis. In recent years, data analysis skills have become essential for those pursuing careers in policy advocacy and evaluation, business consulting and management, or academic research in the fields of education, health, medicine, and social science. This course introduces students to the powerful R programming language and the basics of creating data-analytic graphics in R. From there, we use real datasets to explore topics ranging from network data (like social interactions on Facebook or trade between counties) to geographical data (like county-level election returns in the US or the spatial distribution of insurgent attacks in Afghanistan). No prior background in statistics or programming is required or expected.
The syllabus can be downloaded here.