Learn how to design scalable, trusted data models for modern analytics and machine learning workloads. This course is designed for data analysts, data engineers, and learners who want to understand how data modeling supports consistent, reusable analytics.

You'll compare traditional and modern data modeling approaches, then explore dimensional modeling, star schemas, slowly changing dimensions, and conformed dimensions. Examine normalization, denormalization, and Medallion architecture to evaluate trade-offs in analytical data structures. Discover how One Big Table, graph models, and keys and relationships in DBT influence model design. You'll also explore data governance, data quality controls, feature stores, analytical hierarchies, modeling standards, integrated models, and data APIs. By the end of the course, you'll be able to select appropriate modeling patterns, evaluate architectural trade-offs, design analytics-ready models, and support governed, consistent data access. What makes this course distinctive is its connected approach to foundational modeling, modern data architecture, and governance, helping you make informed design choices for analytics and machine learning.