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.

Applied Learning Project

Learners will complete hands-on projects that demonstrate practical application of Bayesian methods to real-world problems. Projects include implementing MCMC algorithms for parameter estimation, building Bayesian regression models for predictive analytics, developing hierarchical Bayesian models for multi-level data, performing Bayesian model selection and comparison, and applying advanced Bayesian techniques to domain-specific problems in sports analytics and medical decision-making under uncertainty.