Data Analytics and Applied Statistics

Advisory prerequisites: IOE 310, IOE 366, or IOE 373. (3 credits)

This course focuses on fundamental computational methods in data analytics with case studies from real-world applications. The goal is to expose students to a variety of data analytics methods and then demonstrate the applicability of these methods through a diverse set of real-world problems.

Advisory prerequisites: Stats 500 or background in regression. (3 credits) 

Basic design principles, review of analysis of variance, block designs, two-level and three-level factorial and fractional factorial experiments, designs with complex aliasing, data analysis techniques and case studies, basic response surface methodology, variation reduction, and introductory robust parameter designs.

Advisory prerequisites: Math 214 or IOE 366. (3 credits)

This course is an introductory graduate course on data analytics. The course introduces fundamental theories and methods for regression analysis and applications. Topics include multiple regression models, generalized linear models, and nonparametric regression models. Concepts of estimation, inference, diagnostics, transformation, regularization, variable selection, and cross-validation are studied. Students have opportunities to formulate statistical models developed from case studies and to use various computer programs.