DATA101: Data Fundamentals for Analytics and Reporting

Module 1: Data Foundations

This module introduces the foundational concepts of data, databases, and data models. Students explore what data is (and what it is not), how databases differ from spreadsheets, and how data models organize data for retrieval. The module builds from raw data concepts through database structure to normalization, establishing the vocabulary and mental model needed for relational design in Modules 2 and 3.

Lessons

• Data
• Databases
• Data Models
• Core Components of a Data Model
• Normalization

Lab

• Lab 1: Data Model Fundamentals — A small business owner's messy spreadsheet is falling apart. You diagnose what's wrong, identify the entities and data types hiding in the data, and redesign it into properly scoped tables with primary keys.

Module 2: Relationships in the Relational Model

This module teaches students how to design and implement the three relationship types in the relational model. Starting with how to identify relationship types using the two-question method, students progress through implementing one-to-many (foreign keys), many-to-many (intersection tables), and one-to-one relationships. The module concludes with formal normal forms (1NF through BCNF), connecting the intuitive Golden Rule from Module 1 to the formal definitions.

Lessons

• Introducing Relationships
• Implementing One-to-Many
• Implementing Many-to-Many
• Table Participation
• Implementing One-to-One
• Normal Forms

Lab

• Lab 2: Relationships in the Relational Model — You take the tables from Lab 1 and connect them — adding foreign keys, resolving many-to-many relationships, understanding participation as a business rule, and normalizing to BCNF. You leave with a complete relational schema.

Module 3: Dimensional Modeling

This module introduces dimensional modeling as the approach for organizing data in analytical databases. Students learn to distinguish operational from analytical databases, understand the transformations that reshape data from one to the other, and design star schemas with properly structured fact and dimension tables. The module covers advanced dimension concepts including slowly changing dimensions, hierarchies, and snowflaking, and culminates in an end-to-end exercise that takes a normalized operational database through the full transformation pipeline to a completed star schema.

Lessons

• Operational and Analytical Databases
• Transformations
• Dimensional Modeling
• The Star Schema
• Fact Tables
• Dimension Tables
• The Snowflake Schema
• Putting It All Together & Wrap-Up

Lab

• Lab 3: Analytical Data Models — The operational database works, but it can't answer the owner's business questions efficiently. You reshape the normalized schema into a star schema with fact and dimension tables, choose SCD strategies, and trace the full transformation pipeline.