Courses
Experiments and Causal Inference
A graduate seminar on experimental and quasi-experimental designs for causal inference. This course introduces methods widely used in domains such as marketing, information systems, and the social sciences for establishing cause-and-effect relationships from data.
Key Topics
Programming for Business Analytics (Undergraduate)
An undergraduate introduction to data analysis with R for business: wrangling and visualizing data, evaluating causal claims, linear regression, and quantifying uncertainty.
Key Topics
Programming for Business Analytics (Graduate)
A graduate course on empirical analysis with R: from data wrangling and visualization to causal reasoning, regression, and uncertainty quantification, culminating in a research-grade project that prepares students for Experiments and Causal Inference.
Key Topics
The Fall 2026 PBA course websites serve as the digital course syllabi and are updated throughout the semester. Check the relevant Course Homepage regularly for the latest schedule, required readings, lecture slides, and R scripts.