Bayesian Statistics: Excel to Python A/B Testing
By External Provider
About this course
By the end of this course, learners will be able to apply Bayesian statistics for decision-making in both business and healthcare contexts, implement probabilistic models in Excel, and perform advanced A/B and multi-variant testing using Python. The course begins with a hands-on introduction to Bayesian reasoning in Excel, where you will learn to structure datasets, calculate joint and conditional probabilities, and update prior probabilities with real-world healthcare examples. You will practice building Bayesian probability tables, interpreting repeated test outcomes, and analyzing predictive performance for evidence-based decision-making. Next, the course transitions into computational Bayesian statistics with Python. You will gain practical experience with Markov Chain Monte Carlo (MCMC) sampling, approximate posterior distributions using PyMC, and explore hierarchical models for A/B and multi-variant testing. What sets this course apart is its dual approach: simple Excel-based foundations for immediate application, followed by advanced Python implementations for scalable experimentation and machine learning integration.
What you will learn
- Apply Bayesian reasoning in Excel to calculate, update, and interpret probabilities.
- Build probabilistic models and analyze predictive performance in real datasets.
- Use Python with MCMC and PyMC for A/B testing, posterior inference, and scaling.