Machine Learning for Social Scientists

4 credits · Undergraduate

Social scientists and policymakers increasingly use large quantities of data to make decisions and test theories. For example, political campaigns use surveys, marketing data, and previous voting history to optimally target get out the vote drives. Governments deploy predictive algorithms in an attempt to optimize public policy processes and decisions. And political scientists use massive new data sets to measure the extent of partisan polarization in Congress, the sources and consequences of media bias, and the prevalence of discrimination in the workplace. Each of these examples, and many others, make use of statistical and algorithmic tools that distill large quantities of raw data into useful quantities of interest.