This course introduces undergraduates to data analytics and AI, using sports as the motivating application. Through real-world examples from professional sports, students will explore concepts such as exploratory data analysis, regression, classification, clustering, dimensionality reduction, and neural networks. Weekly assignments and a final team project will develop students' skills in using Python-based tools for sports analytics. A light final exam tests key conceptual understanding. Prerequisite: CS 106A (or equivalent Python background) and CS 109 (or equivalent probability background). TA sessions will cover use of pandas and other libraries students will use for projects, as well as freely available sports datasets that could be used.
3 units · Letter (ABCD/NP)
This course introduces undergraduates to data analytics and AI, using sports as the motivating application. Through real-world examples from professional sports, students will explore concepts such as exploratory data analysis, regression, classification, clustering, dimensionality reduction, and neural networks. Weekly assignments and a final team project will develop students' skills in using Python-based tools for sports analytics. A light final exam tests key conceptual understanding. Prerequisite: CS106A (or equivalent Python background) and CS109 (or equivalent probability background). TA sessions will cover use of pandas and other libraries students will use for projects, as well as freely available sports datasets that could be used.
Offered in Autumn 2026 at Stanford University.