Statistical Models and Analysis for Data Science

3 credits · College of Engineering catalogue

Advisory Prerequisite: Introductory statistics and linear regression model (at the level of IOE 366 or equivalent), Linear algebra and matrix algebra. Enforced Prerequisite: None. (3 credits) Offers a comprehensive understanding of both traditional statistical methods and modern machine learning techniques. Topics include fundamental regression models and analysis techniques, along with advanced robust regression methods such as regularization, variable selection, and cross-validation. Additionally, generalized linear models, including the general framework as well as specific models for binomial data, logistic regression, and Poisson regression are explored. A significant emphasis is placed on the practical application of these techniques using statistical software.