This workshop builds a practical understanding of Bayesian modeling through a sequence of examples fitted in Stan. Participants begin with a simple model and extend it step by step to explore priors, likelihoods, posterior inference, prediction, and model checking. Working directly with posterior draws helps connect fitted models to parameter estimates and substantive questions. The session then introduces hierarchical modeling and examines the role of partial pooling. Throughout, the emphasis is on understanding modeling decisions and the workflow that connects them. Participants gain experience interpreting Bayesian results and a foundation for applying Stan-based modeling approaches in their own analytical work.