Overview of data science key concepts and the use of tools for data retrieval, analysis, visualization, and reproducible research. Topics include an introduction to inference and prediction, principles of measurement, missing data, and notions of causality, statistical “traps”, and concepts in data ethics and privacy. Case studies will illustrate the importance of domain knowledge.
Prerequisite: PSTAT 120A; CS 9 or CS 16; and Math 4A, all with letter grade C or better.
Recommended Preparation: PSTAT 120B recommended, but can be taken concurrently.