Overview

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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.

Syllabus

Foundations of Bayesian Machine Learning

This module introduces the core principles of Bayesian statistics and demonstrates their application in supervised machine learning and A/B testing. Learners will explore the fundamentals of Bayesian inference, examine practical examples of decision-making under uncertainty, and gain hands-on experience implementing Markov Chain Monte Carlo (MCMC) methods using PyMC. By the end of the module, participants will develop the ability to connect Bayesian theory with real-world machine learning experiments.

Data Exploration and Preparation

This module introduces learners to the fundamentals of preparing healthcare datasets for Bayesian statistical modeling using Microsoft Excel. Learners will explore project goals, understand the structure of real-world healthcare testing data, and create efficient summaries for initial analysis. By examining historical, future, demographic, and center-based trends, students will gain the ability to organize, interpret, and structure data effectively, ensuring a strong foundation for Bayesian probability applications in healthcare analytics.

Bayesian Modeling and Application

This module guides learners through constructing and applying Bayesian probability tables in Microsoft Excel to analyze healthcare testing scenarios. Students will learn how to structure Bayesian frameworks, calculate joint probabilities, update prior probabilities with new evidence, and interpret outcomes across multiple testing cycles. By the end of this module, learners will be able to apply Bayesian reasoning to real-world healthcare data, enhancing accuracy in predictive healthcare analytics.