Big Data and Better Decisions

Undergraduate / Graduate

This course will introduce students to advanced methods for data driven decision making with an emphasis on business applications. Students will learn how to build and interpret models that address two fundamental categories of business questions: (i) causal analysis and (ii) forecasting and prediction. The first portion of the course will cover experimental design, as well as non-experimental causal inference (e.g. matching, fixed effects, differences-in-differences, synthetic control). The second portion of the course will focus on machine learning topics including linear regularization, cross validation, tree models, random forests and boosting. The course will also explore cutting edge methods at the intersection of causal inference and machine learning. Heavy emphasis will be placed on discussion of real examples and business applications of these methodologies. The course work will include writing code and analyzing data in R to learn how these techniques are implemented in practice.