Statistical Methods for Data Science surveys the statistical methods most useful in data science applications. Topics covered include predictive modeling methods, including multiple linear regression, and time series, data dimension reduction, discrimination and classification methods, clustering methods, and committee methods. Students will implement these methods using statistical software.
Prerequisites
Statistics at the level of MA 2611 and MA 2612 and linear algebra at the level of MA 2071.