Data Quality Fundamentals with Great Expectations

Maintaining high data quality is essential for accurate analytics, reliable machine learning models, and trustworthy reporting. This text-based course introduces core data quality principles and guides you through implementing automated data testing using Great Expectations. You will gain a clear understanding of how to detect anomalies, prevent data pipeline failures, and establish data observability in modern data architectures. What you'll learn: - Understand key data quality dimensions, terminology, and foundational concepts - Create and execute data validation suites using Great Expectations in Python - Detect schema drift, missing values, and invalid data types automatically - Document dataset expectations and generate readable data quality reports - Integrate automated quality checks into modern data pipelines and workflows - Apply best practices for data observability and ongoing quality monitoring. The course begins with fundamental definitions, exploring why data quality matters and how validation fits into modern engineering setups. You will then progress through step-by-step written explanations and practical code examples to build, run, and maintain custom expectation suites. This course is designed for aspiring data engineers, data analysts, and developers looking to ensure data reliability. No prior experience with data quality tools is required, though basic Python familiarity is recommended. Start reading today to build dependable data pipelines and take control of your data quality.