STAT 427 - Statistics for Data Science

Credits: (3)

Instructional Method: Lecture
Prerequisites: STAT 302
A modern statistics course for data science with emphasis on computation, inference, and reproducibility. Core topics include simulation and resampling, generalized linear models, penalized regression (ridge, lasso), computational
inference methods, and Bayesian approaches. Students use a statistical programming language such as R or a similar platform for analysis, visualization, and reproducible workflows, with applications drawn from real-world datasets. Ethical and inclusive data practices are emphasized throughout.