A/B Testing Data Sourcing and Pipeline Fundamentals

High-quality experimentation depends on trustworthy data pipelines and accurate tracking from the start. This course equips you with the foundational principles of sourcing, preparing, and validating data specifically for A/B testing and experimentation programs. By working through clear written explanations and structured examples, you will learn how to define tracking requirements, clean raw event logs, and build experiment datasets that yield dependable statistical insights. What you'll learn: Understand core terminology, statistical fundamentals, and the data lifecycle in A/B testing; Define robust tracking specifications and event schemas across user journeys; Collect and source experimental data using modern event pipelines and database queries; Identify and mitigate common data quality pitfalls such as sample ratio mismatch and tracking drops; Clean, aggregate, and structure raw test logs for conversion and metric analysis; Apply modern privacy practices and consent handling into experimentation data workflows. The course begins with essential terms, metric definitions, and data pipeline fundamentals before progressing through practical tracking architecture, data cleaning methods, and validation routines. This guide is designed for beginners, product analysts, marketers, and aspiring data practitioners with no prior experimentation experience required. Start building dependable data foundations for your experiments today.