With the exponential growth in computing power, Bayesian analysis has metamorphosed from an obscure alternative to a serious challenger of the traditional frequentist statistics. This workshop introduces the conceptual background, computational procedures, and statistical techniques for doing Bayesian regression analysis, with a focus on application and interpretation. Selected topics covered include the historical background of the Bayesian framework in comparison with the classical frequentist approach, the Bayes’ theorem, the basics of likelihood theory, the Markov chain Monte Carlo (MCMC) methods, Bayesian generalized linear regression and advanced models, and post-estimation analyses. The first component of each lecture covers important concepts and techniques, and the second component teaches the workflow of doing Bayesian data analysis using R along with JAGS and Stan.