Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course empowers you to harness AI and modern tools for effective data modeling, improving your ability to design, analyze, and optimize databases and analytics systems. You'll gain practical experience using ChatGPT and GitHub Copilot to accelerate data model creation and documentation. The journey begins with foundational concepts, covering conceptual, logical, and physical data models, dimensional modeling, additivity, conformed and slowly changing dimensions, normalization, and star schema design. You will explore practical demonstrations in Power BI and diagramming tools, ensuring a strong grasp of data modeling principles. This course is ideal for data analysts, BI professionals, developers, and anyone seeking to leverage AI-assisted tools for data modeling. No prior experience is required, though familiarity with databases is helpful. By the end of the course, you will be able to design conceptual, logical, and dimensional models, implement ETL pipelines, document schemas, perform data quality checks, and integrate AI tools to streamline modeling workflows.

Syllabus

Introduction

In this module, we will provide a high-level overview of the course. You will meet the instructor and learn what to expect throughout the course. This section sets the foundation for your journey in AI-driven data modeling.

Data Modeling Fundamentals and Foundations

In this module, we will explore conceptual, logical, and physical data models. You will learn dimensional modeling, additivity, SCDs, star schema, and normalization. Practical demonstrations in Power BI will illustrate these concepts in action.

Data Modeling Exercise

In this module, we will work on a hands-on case study to implement data models. You will use popular modeling tools to create conceptual, logical, and dimensional models. The exercises reinforce practical skills in real-world data modeling.

Data Architecture Essentials

In this module, we will introduce core concepts of data architecture. You will explore BI, data warehouses, marts, lakes, and lakehouses. The section emphasizes architecture design, roles, and governance for effective analytics.

Extract Transform Load (ETL), ELT, and OLAP & OLTP

In this module, we will cover ETL/ELT, OLAP/OLTP, and system architecture impact on data models. You will learn batch and real-time processing, orchestration, and performance optimization. The section provides hands-on concepts for efficient data workflows.

ETL and Data Architecture Exercise in Apache Hop

In this module, we will apply ETL concepts using Apache Hop. You will set up the environment, develop pipelines, and move data into the warehouse. The section prepares you to integrate ETL with Power BI for analysis.

Capstone Project – From Requirements to Data Model, to ETL to Power BI

In this module, we will apply all course concepts in a capstone project. You will analyze requirements, design models, develop ETL pipelines, and visualize data in Power BI. This section consolidates practical skills for real-world application.

Data Modeling with Artificial Intelligence (AI), Copilot and ChatGPT

In this module, we will integrate AI tools into data modeling tasks. You will leverage ChatGPT and Copilot for brainstorming, documentation, ETL automation, and data validation. The section demonstrates modern AI-driven modeling workflows.

Conclusion

In this module, we will wrap up the course and highlight critical takeaways. You will review the learned techniques and explore their application in practical scenarios. This section reinforces knowledge and guides future growth.