Learn how to transform raw data into reliable, analytics-ready datasets and design scalable data models for reporting, analytics, and machine learning. This course is designed for learners interested in modern data transformation, data modeling, and data quality.

You'll compare ETL and ELT approaches, explore data normalization, and learn how fact tables and slowly changing dimensions support structured analytics. Examine idempotent transformations, missing-data handling, aggregation strategies, schemas, semantic layers, and metrics stores to improve data reliability and consistency. Discover how Change Data Capture (CDC), temporal modeling, data mesh, and data products support evolving and distributed data. You'll also explore metadata, model testing, anonymization, documentation, versioning, and feature stores to strengthen data governance and maintainability. By the end of the course, you'll be able to evaluate transformation approaches, apply modeling techniques, improve data quality, and manage evolving data models for analytics and machine learning workflows. What makes this course distinctive is its integrated approach to data transformation, scalable modeling, reliability, and governance, helping you build a strong foundation for trustworthy, reusable analytics.