Applied Bayesian Data Analysis

Master advanced Bayesian inference techniques and their practical applications in data science. This course will equip you with cutting-edge methods, including variational inference, Bayesian decision theory, and non-parametric approaches. You'll learn to quantify uncertainty in predictions, make principled decisions using loss functions, and implement flexible models that adapt complexity to data. Through hands-on projects using PyMC3 and real-world case studies, you'll develop expertise in the complete Bayesian workflow: from model specification to validation. The course emphasizes scalable alternatives to MCMC, including variational inference for large datasets, and covers advanced topics such as Dirichlet processes and Gaussian process regression. What makes this course unique is its focus on practical implementation and decision-making under uncertainty. You'll gain skills in probabilistic programming, model evaluation, and applying Bayesian methods to diverse domains. By completing this course, you'll be equipped to tackle complex data problems with rigorous statistical methods and communicate uncertainty effectively in professional settings.

Master Bayesian inference and unlock powerful probabilistic reasoning for data-driven decision-making. This course builds your foundation in Bayesian analysis, from viewing probability as degrees of belief to implementing advanced MCMC methods. Learn to apply Bayes’ theorem to real-world problems, use conjugate priors for efficient computation, and derive credible intervals that fully capture parameter uncertainty. Through hands-on practice, you’ll move from analytical solutions to computational techniques like Metropolis-Hastings, Gibbs sampling and Variational Inference, essential for modern Bayesian workflows. You’ll gain skill in interpreting posterior distributions, contrasting Bayesian and frequentist perspectives, and applying convergence diagnostics for reliable results. Whether in finance, healthcare, or business, you’ll acquire the statistical framework and computational tools to make principled inferences under uncertainty and effectively communicate probabilistic insights.