Applied Bayesian Data Analysis with R

Applied Bayesian Data Analysis with R udemy course

Build powerful statistical models, simulate uncertainty, & master Bayesian thinking using real data & modern tools in R

This comprehensive course will take you on a journey into the world of Bayesian statistics, one of the most powerful and intuitive frameworks for reasoning under uncertainty. Using real data and hands-on coding in R, you'll learn how to build, evaluate, and interpret Bayesian models using cutting-edge tools like brms and Stan.

Whether you're a data scientist aiming to deepen your statistical toolkit, a social scientist wanting to model complex effects, or a beginner curious about Bayesian reasoning, this course is designed to bring clarity, confidence, and capability to your analysis.

We begin by reshaping how you think about probability. Rather than treating it as a long-run frequency, you’ll learn to think of probability as a degree of belief—a perspective that naturally leads to Bayesian reasoning. Through intuitive explanations and code-based demonstrations, we’ll explore how prior beliefs can be updated using new data to form posterior conclusions.

From there, we move into core computational methods that allow modern Bayesian analysis to scale. You’ll master Markov Chain Monte Carlo (MCMC) sampling—starting with the Metropolis-Hastings algorithm and moving toward Hamiltonian Monte Carlo (HMC) and NUTS, the algorithms that power modern Bayesian engines like Stan.

But theory alone isn’t enough.

That’s why this course is packed with practical, real-world applications using the powerful and user-friendly brms package in R—a front-end to Stan that lets you fit sophisticated models using familiar R syntax. You'll build Bayesian linear regressions, simulate data, check assumptions, and interpret your results like a pro.

For those ready to go deeper, we’ll open the hood and dive into writing models directly in Stan, giving you complete control over model structure, likelihoods, and priors. You’ll explore everything from simple linear models to non-linear growth curves and hierarchical structures.

We’ll also equip you with the tools needed for model validation and selection, including posterior predictive checks, cross-validation, and Expected Log Predictive Density (ELPD). You’ll learn how to diagnose convergence issues, identify divergent transitions, and follow a principled Bayesian workflow from model formulation to decision-making.

By the End of This Course, You Will:

• Be able to think like a Bayesian, incorporating prior knowledge and updating beliefs using data

• Confidently use MCMC methods to fit and diagnose Bayesian models

• Perform Bayesian regression analysis using brms, and know when and how to customize models using Stan

• Understand how to simulate from priors and posteriors, check model fit, and communicate uncertainty clearly

• Apply a principled Bayesian workflow to real-world data problems, from data exploration to final model validation