Foundations of Probability and Statistics in Data Science

4 credits

Foundations of probability and statistics needed for data science, including mathematical theory, and hands-on experience of applying this theory to actual data using Jupyter notebooks. Random variables, dependence, correlation, regression, PCA, entropy and MDL. This course is a distance education course. Prerequisites: none. Restricted to major codes DS78 and DS79. All other students with graduate standing may be considered as space permits.