Applied Bayesian Data Analysis

This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.