A/B Testing and Experimentation for Data Analysts

Organizations rely on data-driven decisions to grow, and A/B testing is the primary tool used to validate new features and strategies. Understanding how to run controlled experiments is an essential skill for anyone looking to work in data science, product management, or business analytics. This course provides a comprehensive foundation in the experimentation lifecycle, moving from core statistical theory to practical implementation and business impact. You will learn how to move beyond guesswork by using rigorous methodology to measure the effect of changes on user behavior. What you'll learn: - Understand the fundamental terminology and statistical concepts behind controlled experiments - Calculate required sample sizes and statistical power to ensure reliable and valid results - Design robust A/B tests that align with business KPIs and specific product goals - Analyze experimental data using Python and interpret p-values and confidence intervals - Identify common pitfalls such as novelty effects, selection bias, and interference - Explore modern approaches including Bayesian testing and automated experimentation workflows The course begins with core definitions and the scientific method before progressing to technical setup and post-test analysis. You will read through the entire process of running a test on a digital product, including how to evaluate results and communicate findings to stakeholders effectively. This course is designed for aspiring data scientists, analysts, and product managers who are new to experimentation. No prior experience with A/B testing is required, though a basic understanding of Python is helpful. Start building the skills to lead data-driven experimentation in any organization.