Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest.

Data analysis in R
Fundamentals of probability, random variables, and joint distributions
Collecting and describing data
Joint and conditional distributions of random variables
Joint, marginal, and conditional distributions, and functions of random variables
Special distributions, the sample mean, the central limit theorem, and estimation
Assessing and deriving estimators, confidence intervals, and hypothesis testing
Causality, analyzing randomized experiments, and nonparametric regression
Single and multivariate linear models
Practical issues in running regressions and omitted variable bias
Endogeneity, instrumental variables, and experimental design
Machine learning and data visualization