Bayesian Methods and the Statistics and Data Science Curriculum

Abstract

The Journal of Statistics and Data Science Education recently published a cluster of papers on Bayesian methods (https://www.tandfonline.com/toc/ujse20/28/3?nav=tocList). The Bayesian cluster includes a presentation of how and why Bayesian ideas should be added to the curriculum; guidance on how to structure a semester-long Bayesian course for undergraduates; a discussion of the ever-evolving nature of Bayesian computing; a book review; and a panel interview of several Bayesian educators.
For the February CAUSE/JSDSE webinar series, we’ve invited several authors of these provocative and informative articles to describe their work and its implications for statistics and data science education.

Jingchen (Monika) Hu is an Assistant Professor of Mathematics and Statistics at Vassar College. She teaches an undergraduate-level Bayesian Statistics course at Vassar, which is shared online across several liberal arts colleges. Her research focuses on dealing with data privacy issues by releasing synthetic data.

Kevin Ross is an Associate Professor of Statistics at Cal Poly San Luis Obispo. His research interests include probability, stochastic processes and applications as well as probability and statistics education.

Colin Rundel is a lecturer at the University of Edinburgh and an assistant professor of the practice in Statistical Science at Duke University. His research interests include applied spatial statistics with an emphasis on Bayesian statistics and computational methods.