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P2-01: Bayesian Courses for Data Scientists and Cross-Campus Share


By Jim Albert, Jingchen (Monika) Hu (Bowling Green State University)


Abstract

Bayesian methods provide new perspectives and inferential techniques for data scientists. One of the authors recently designed a new “Beginning Bayes” course for Datacamp that introduces the basic tenets of Bayesian inference and contrasts these methods with traditional inference. The online course consists of video lectures, exercises using the statistical system R, and the use of a new R package TeachBayes to illustrate Bayesian concepts through graphs and tables. Due to staffing constraints, advanced-level undergraduate courses such as Bayesian statistics can rarely be offered at small liberal arts colleges. As part of the pilot study of the Upper Level Math/Stats Project, one of the authors taught Vassar College’s Bayesian Statistics to multiple campuses through a shared/hybrid model on the Liberal Arts Consortium for Online Learning (LACOL) platform. The online course used software to provide synchronous and asynchronous access to the lectures, held online office hours for remote students, coordinated with local faculty from each remote campus, and created a learning community for all students. One important aspect of the Bayesian course was the use of R extensively for simulations and data analysis.


Recording

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