Bayesian Statistics and A/B Testing with Excel and Python

Traditional statistical methods often fall short when making fast-paced decisions under uncertainty. This course introduces you to Bayesian statistics, a powerful framework for updating your knowledge as new information becomes available. You will gain the skills to move from simple spreadsheet calculations to robust Python scripts for sophisticated A/B testing. You will learn how to quantify uncertainty and update your beliefs as new data arrives, transforming how you approach experiments in business or research environments. By focusing on probabilistic reasoning, you can make more nuanced decisions than traditional p-values allow. What you'll learn: - Understand core Bayesian terminology and the fundamental principles of probability. - Calculate joint and conditional probabilities using practical Excel models. - Apply Bayes' Theorem to update prior beliefs with new experimental evidence. - Implement Bayesian A/B and multivariate testing models using modern Python libraries. - Analyze posterior distributions to quantify risk and certainty in your results. - Practice interpreting statistical outcomes for data-driven decision-making in business and healthcare. The course begins with foundational definitions and probability theory before progressing through written explanations of Excel-based modeling and Python-based automation. You will work through text-based exercises that reinforce your understanding of how to structure and analyze experimental data. This course is designed for beginners, analysts, and aspiring data scientists who want to enhance their experimental toolkit. No previous experience with Bayesian statistics is required. Enhance your analytical skills by mastering the Bayesian approach to experimentation.