Introduction to Bayesian Statistics for Data Science

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data science problems. Topics include the use and interpretations of probability theory in Bayesian inference; Bayes’ theorem for statistical parameters; conjugate, improper, and objective priors distributions; data science applications of Bayesian inference; and ethical implications of Bayesian statistics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Scienc