Practical A/B Testing with Python: Design and Analyze Experiments

Making product changes without data is a gamble. A/B testing is the industry-standard methodology used to validate new features, optimize user experiences, and drive business growth with statistical certainty. This written course guides you from the absolute basics of experimentation to a confident practitioner. You will learn how to frame business problems, select the right metrics, design statistically robust experiments, and analyze the outcomes using Python. By working through clear explanations, conceptual breakdowns, and structured code snippets, you will gain the practical skills needed to make reliable, data-driven decisions. What you'll learn: - Understand the foundational concepts of hypothesis testing, significance levels, and statistical power. - Define key experimental metrics, including primary, secondary, and guardrail metrics, to measure success accurately. - Calculate required sample sizes and design experiments that avoid common pitfalls like the novelty effect and selection bias. - Analyze experimental data using Python libraries such as Pandas and Statsmodels to determine statistical significance. - Apply modern best practices to address advanced experimentation challenges, including sequential testing and network effects. The course begins with core statistical definitions and metric-setting principles before transitioning into experimental design. You will then progress to analyzing real-world scenarios through step-by-step Python code walkthroughs. This course is designed for aspiring data analysts, product managers, and beginners looking to understand experimentation without needing prior advanced statistical knowledge. Start learning today to turn raw data into actionable product insights.