This course provides a basic training of Bayesian statistics. Some ideas and principles of Bayesian including prior and posterior distributions, conjugate priors, Bayesian estimates, empirical Bayes, Bayesian hypothesis testing, Bayesian model selection and Bayesian networking are covered. Other Bayesian tools such as Bayesian decision theory, Bayesian data analysis, and Bayesian computational skills will also be discussed. An open-source, freely available software R will be used to implement these computational and data analytics skills. Hands-on experience and case studies such as pattern recognition and spam filtering will also be provided to students. Completion of this course will give students access to a wide range of Bayesian analytical tools, customizable to real data.