Data cleaning and validation

Learn essential techniques for cleaning and validating basketball data to ensure accuracy and reliability in your analytics. This topic covers identifying and handling missing values, detecting outliers, validating statistical relationships (e.g., FGM ≤ FGA), standardizing data formats, and implementing automated quality checks. You'll learn how to build robust data validation pipelines that catch errors early and maintain data integrity.