Modern Data Quality Management with AI and Machine Learning

Reliable decision-making requires trustworthy data, yet traditional rule-based data validation struggles to keep pace with modern data volumes. This text-based course guides you through applying artificial intelligence and machine learning techniques to automate data profiling, monitor pipeline health, and proactively resolve data issues. You will read through clear explanations, conceptual breakdowns, and practical workflow examples that show you how to design intelligent quality checks across modern data architectures. What you'll learn: - Understand core data quality dimensions and fundamental machine learning concepts used in data governance - Implement automated data profiling and schema drift detection techniques - Apply machine learning models for anomaly detection and outlier identification in structured datasets - Configure automated data cleansing, deduplication, and entity resolution workflows - Monitor data pipeline observability and establish continuous quality feedback loops - Evaluate modern data governance frameworks to ensure compliance, trust, and reproducibility The course begins with foundational terminology, baseline metrics, and essential definitions before advancing to automated quality architectures and practical implementation strategies. It is designed for beginners, data analysts, and aspiring data engineers who want to modernize their data management practices without requiring advanced math or deep coding prerequisites. Start reading today to build robust, automated data quality systems that scale with your organization.