Data Quality Fundamentals

Poor data quality leads to costly mistakes, flawed analyses, and unreliable business decisions. Understanding how to identify, measure, and resolve data issues is a critical skill for anyone working with data. This course guides you through the essential principles of data quality management, helping you progress from understanding core dimensions of data health to implementing systematic profiling and cleaning techniques. What you'll learn: - Understand the core dimensions of data quality, including accuracy, completeness, consistency, and timeliness - Analyze datasets using profiling techniques to discover anomalies, missing values, and structural inconsistencies - Apply structured data cleaning workflows to resolve common formatting and integrity issues - Explore modern data observability concepts to monitor data health and detect anomalies automatically - Design basic data quality metrics and validation rules to maintain high standards over time You will begin by learning fundamental definitions and terminology before moving on to practical profiling techniques, programmatic cleaning workflows, and modern monitoring practices through written explanations and structured text-based exercises. This course is designed for beginners, aspiring data analysts, and business professionals who want to ensure their data is trustworthy, with no prior technical experience required. Start building your foundation in data quality today and turn raw, messy datasets into reliable assets.