A thorough introduction to statistics from a Bayesian perspective. Methods covered will include: the rules of probability, including joint, marginal, and conditional probability; discrete and continuous random variables; discrete and continuous random variables; Bayesian inferences for means and proportions; the simple linear regression model analyzed in a Bayesian manner; and (time permitting) a brief introduction to numerical methods such as the Gibbs sampler. Throughout the course, R will be used as software, a brief introduction will be given in the beginning.