This workshop will serve as an introduction to the mathematical and programming skills necessary to implement a few popular techniques in Bayesian econometrics. The course will begin with a motivation of the Bayesian approach to statistical inference as well as a quick tour of modern posterior sampling methods using the R programming language. We will then review some standard models for cross-sectional data, including Linear Regression, Quantile Regression, as well as Generalized Linear Models for count or binary data. We conclude with an introduction to hierarchical models, where Bayesian inference may hold some comparative advantage over alternative methods. This is presented through the lens of several interesting problems that are common in applied research, such as measurement error, missing and incomplete data, as well as multi-level models with mixed effects and information pooling. Each lecture is accompanied by a programming tutorial emphasizing practical workflow considerations, including prior and posterior predictive simulation, model validation and diagnostic criteria. Knowledge of econometrics, probability theory and statistical programing at an introductory level is assumed but not strictly required. Course content is also flexible depending on participant background and interest.