Advanced Data Science

Catalog description

Introduces some advanced topics in statistical modeling and data science. The course covers a broad range of topics, starting from diagnostics and variations of the ordinary least-squares regressions such as non-normality, nonlinearity, nonconstant error variance, collinearity, and more. The course then proceeds to maximum likelihood estimations and related models such as the Logit regression and Probit regression. It further investigates time-series-cross-sectional data, multilevel models with random effects and fixed effects, and event-history modeling techniques. Finally, causal inference methods such as A/B testing, Difference-in-Difference estimation, and matching are discussed. Prerequisite(s): SECR 7605 Lecture Hours: 3 Lab Hours: 0 Other Hours: 0 Contact Hours: 3 Grade Mode: Normal (A, B, C, D, F) Degree Restrictions: Doctor of Philosophy Schedule Type (Primary): Lecture Schedule Type (Additional): Seminar Small Group Click here for the Schedule of Classes.