Bayesian statistics for confused data scientists

A practical introduction to Bayesian statistics aimed at data scientists familiar with frequentist methods. Explains the philosophical difference between Bayesian and frequentist approaches using a dice-rolling example, covering priors, likelihoods, posteriors, and credible intervals. Demonstrates how MCMC methods (via PyMC) make Bayesian inference tractable in practice, including linear regression with outlier handling. Also reveals that common regularization techniques like Lasso and Ridge regression are equivalent to MAP estimation with Laplace and Gaussian priors respectively.