Advanced Data and Decisions: Causality and Prediction for Business

3 credits · GSB

This is a course in data analysis that satisfies the core Data and Decisions requirement in the Advanced tier. The course assumes students have prior training in introductory statistics covering basic probability, hypothesis testing, and linear regression. This version of the course emphasizes applications in business economics and broadly covers two topics: (i) causal data analysis, answering actionable business questions such as: "how much more would an Amazon customer spend if they were to join Prime? and (ii) prediction using ML models. In roughly half of the classes, students work on hands-on, real-world applications, learning how to use state-of-the-art AI tools to obtain, manipulate, and analyze data. In remaining classes, students learn conceptual material through class discussions of examples taken from business cases and academic papers. A key component of the course is a group project. All coding will be done by "vibe coding" using Claude Code, and a mix of mostly Python and R (student choice - prior knowledge useful but not required).