Master Bayesian Statistics: Apply, Implement & Optimize A/B Testing equips you with the knowledge and practical skills to apply Bayesian statistics to machine learning, A/B testing, and healthcare analytics. Throughout the course, you will build a solid foundation in Bayesian inference, learn how probabilistic thinking supports decision-making under uncertainty, and implement Markov Chain Monte Carlo (MCMC) sampling using PyMC to approximate posterior distributions.

As you progress, you will apply hierarchical Bayesian models to evaluate A/B and multi-variant testing scenarios and gain practical experience organizing and preparing healthcare datasets using Microsoft Excel. You will analyze historical, demographic, predictive, and center-based trends, construct Bayesian probability tables, calculate joint probabilities, update prior beliefs with new evidence, and interpret predictive outcomes across repeated testing cycles. Designed for learners interested in Bayesian statistics, machine learning, A/B testing, and healthcare analytics, this course bridges statistical theory with practical implementation. Its structured, end-to-end approach takes you from the fundamentals of Bayesian inference through computational modeling and real-world applications, enabling you to confidently apply Bayesian methods for data-driven analysis, experimentation, and predictive decision-making.