Statistics for Data Science II


This builds on the foundation from the first course (SDS I) and further develops the theory of statistical hypotheses testing. It also covers advanced computer intensive statistical methods, such as the Bootstrap, that will make extensive use of R. The emphasis of the course is to expose students to modern statistical modeling tools beyond linear models that allow for flexible and tractable interaction among response variables and covariates/feature sets. Statistical modeling and analysis of real datasets is a key component of the course. Prerequisites: SDS I and Math 439 (Concurrent enrollment in Math 439 is fine).
Course Attributes: AS NSM; AS AN

Section 01

Statistics for Data Science II
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