Data Modeling for Analytics, AI and Modern Data Systems

Course Description

Formerly "Data Modeling, Introduction."

Data modeling defines and applies structure to the information systems in an enterprise. Data stored in various relational databases needs data modeling to depict the relationship between entities in the databases. The models provide pictorial views of how the data flows across the enterprise, departments, or business areas. Before creating a database for any application, you need well-constructed data models to maintain the integrity of data and improve query performance.

This course provides in-depth knowledge and hands-on practice in data modeling and design. After introducing the basic concepts and principles, the course addresses data modeling techniques and practices in four modeling areas: conceptual, logical, physical, and dimensional. The course first addresses the collection of user requirements, followed by design approaches for logical and physical models.

You will study real-world examples of data models for transactional systems, data marts, enterprise data warehouses, and modern analytics pipelines. Expert instructors will share their practical experiences connecting foundational data modeling skills to today's data stack-including cloud data warehouses, machine learning pipelines, and AI-driven applications. This is a hands-on course using an industry-leading data modeling tool in class. By the end of the course, you will be able to create data models for enterprise applications and understand how your modeling decisions impact analytics accuracy and AI system behavior.

Learning Outcomes  
At the conclusion of the course, you should be able to

• Describe the various types and advantages of Data Modeling
• Discuss the quantifiable values of Data Modeling
• Explain the intricacies of Data Modeling
• Identify the use cases for Data Modeling
• Evaluate how data modeling decisions affect analytics accuracy, machine learning performance, and AI system outputs
• Assess and improve AI-generated data model designs using foundational modeling principles

Topics Include

• Overview of data modeling
• Principles of data modeling
• Types of data modeling: Conceptual, Logical, and Physical
• Logical data modeling: Building data models; Cardinality rules; Transformation rules
• Physical data modeling: Database standards; Domains and classwords; Roll-ups and roll-downs; Data model repository options
• Dimensional data modeling: Star schema modeling; Snow flake modeling
• Data modeling for modern analytics stacks: cloud data warehouses; semantic layers; analytics and engineering concepts
• Data modeling for AI and machine learning: feature table design; data quality and bias; AI-generated schema evaluation

Additional Information

AI* - This course has students apply AI-assisted design and validation tools to evaluate data models, generate schema recommendations, and improve conceptual, logical, and physical modeling accuracy. Students will also critically assess where AI-generated models break down, deepening their understanding of real-world data architecture, accelerating hands-on learning, and building the judgment needed to use AI tools responsibly in data engineering and analytics roles.