Data Science for Software Engineering

CS 588 treats software engineering itself as a domain to be studied empirically: instead of writing the software, you mine the artifacts it leaves behind (commits, bug reports, code review traces, developer activity) to ask what actually makes teams and codebases work. Most of the semester is spent reading and critiquing research papers, presenting a survey, and carrying out a term project where you pose a question, pull data from real repositories, and defend your methodology. It's a graduate-level entry point into MSR-style research, useful if you want to do a thesis on developer productivity, recommender systems for code, or the empirical side of AI-assisted programming.