Data Quality: Measure, Improve, and Enforce Reliable Systems

Data quality is the foundation of reliable analytics, trustworthy dashboards, and effective machine learning. In this course, instructor Smriti Mishra explores the core concepts of data quality and shows you how to apply best practices to improve outcomes across the data lifecycle. From understanding quality dimensions to building validation checks in Python and integrating them into data pipelines, this course equips you with practical tools for managing data quality more effectively in real-world environments. Along the way, Smriti highlights how poor-quality data can undermine AI models, distort business intelligence, and compromise strategic decision-making. This course is designed for data professionals who want to create more trustworthy, efficient, high-performing data systems.

Learning objectives

• Evaluate data quality using key dimensions such as accuracy, completeness, and consistency.
• Identify common causes of poor data quality and their real-world consequences.
• Apply profiling and validation techniques to assess data quality using code and tools.
• Integrate data quality checks into data pipelines and analytics workflows, and analyze their impact on machine learning models, dashboards, and business decisions.
• Design sustainable systems that promote and enforce high-quality data over time.