Course Objectives

Upon completion of this course, students will be able to:

• Understand the fundamental concepts of data, information, and data modeling.
• Design and interpret Entity-Relationship Diagrams (ERDs) to represent business requirements.
• Apply normalization techniques (1NF, 2NF, 3NF, BCNF) to reduce data redundancy and improve data integrity.
• Differentiate between relational and dimensional modeling and select the appropriate approach for a given scenario.
• Design and implement dimensional models, including star and snowflake schemas, for data analytics and business intelligence.
• Use data modeling tools to create and maintain data models.
• Articulate the importance of data modeling in software development, data analytics, and business intelligence.

Target Audience

This course is ideal for aspiring data analysts, data engineers, business intelligence developers, database administrators, software developers, and any beginners who want to deepen their understanding of data structure and management.

Course Schedule

Week 1

Introduction to Data Modeling

• What is Data Modeling?
• The Importance of Data Modeling for Software Development, Data Analytics, and BI.
• The Different Types of Data Models (Conceptual, Logical, Physical).
• Introduction to Key Concepts: Entities, Attributes, Relationships.

Relational Data Models and Database Design

• Fundamentals of the Relational Model.
• Tables, Columns, and Rows.
• Keys: Primary Keys, Foreign Keys, and Composite Keys.
• Relational Integrity: Entity Integrity and Referential Integrity.

Entity-Relationship (ER) Modeling

• Components of an ER Diagram: Entities, Attributes, and Relationships.
• Cardinality: One-to-One, One-to-Many, Many-to-Many Relationships.
• Practice: Creating a simple ER Diagram from a business problem.
• Introduction to Crow's Foot and other ERD notations.

Week 2

Normalization

• The Purpose of Normalization: Reducing Data Redundancy.
• The Rules of Normal Forms:
• De-normalization: When and why to break the rules.

Dimensional Modeling for Data Analytics

• Introduction to Dimensional Modeling.
• Fact Tables and Dimension Tables.
• The Star Schema: Design and Benefits.
• The Snowflake Schema: Design and Trade-offs.
• Defining Measures and Attributes.

Data Warehousing Concepts

• What is a Data Warehouse?
• The Role of Data Modeling in a Data Warehouse.
• ETL (Extract, Transform, Load) Processes.
• Slowly Changing Dimensions (SCD) Types.

Week 3

Data Modeling Tools and Best Practices

• Overview of popular data modeling tools (e.g., ER/Studio, DBeaver, Lucidchart).
• Best Practices for Naming Conventions, Documentation, and Version Control.
• Collaboration and Communication in a Data Modeling Team.

Final Project & Case Study

• Students will be presented with a real-world business case.
• Task: Design a logical and physical data model to solve the business problem.
• Presentation of the data model and defense of design choices.

Assessment

• Project Work (100%) Students will start working on their project from the beginning of the first class.