Computational Statistics and Probability

Catalog description

Arguably, most of data science is statistical learning, which requires strong foundational knowledge in probability and statistics. And applying computational methods such as direct simulation, shuffling, bootstrapping, and cross validation to statistical problems is often more intuitive, and intuitive and can provide solutions where analytical methods would prove computationally intractable. This course introduces students to the statistical analysis of data using modern computational methods and software. Probability, descriptive statistics, inferential statistics and computation methods such as simulations sample distributions, shuffling, bootstrapping, and cross-validation will be covered. Open to graduate students. Undergraduate students may take this course as a Pathways student through the dual degree BA/MA program or take it as undergraduate credit only by filling out the Request to Take Graduate Course for Undergraduate Credit form, available on the Registrar's Office website. Crosslisted with AIM 5002 .