Practical A/B Testing and Statistical Analysis in Python

Businesses rely on experimentation to optimize user experiences and increase revenue. Understanding how to structure and interpret these tests is a core skill for any data professional. You will move from understanding basic statistical concepts to implementing full A/B testing workflows in Python. You will gain the skills to define experimental goals, ensure statistical validity, and present actionable insights. What you'll learn: - Define primary and secondary metrics that align with business objectives - Calculate required sample sizes and test durations using power analysis - Implement hypothesis tests using libraries like scipy and statsmodels - Understand p-values, confidence intervals, and statistical significance - Analyze results while accounting for common pitfalls like the multiple comparisons problem - Explore modern approaches including Bayesian A/B testing for faster decision-making The course begins with foundational statistical theory and experimental design before transitioning into practical Python implementation and results interpretation. This course is designed for beginners interested in data analysis, marketing, or product management; no prior experience with A/B testing is required. Start building your skills in data-driven experimentation today.