Governments, companies, and charities are increasingly using “big" data to guide decision making. This course builds up foundational concepts and skills to help data scientists answer questions about the economic world. In the first half of the course we learn the fundamentals of data analysis in R, causal inference, and prediction. In the second half we learn what makes a dataset “big” and how to make accurate predictions about economic variables when the data contains many predictors. We will use real datasets to study topics which may include: the markups charged by online retailers, rents charged by airbnb units, salaries paid to athletes, and the prices of used cars. No prior experience with R or coding is necessary or expected.