SQL for AI-Driven Data Insights
Course Objective
This course is designed to build foundational and advanced SQL skills for extracting, analyzing, and visualizing data in AI-driven environments. Students will learn how to use SQL for business intelligence, machine learning pipelines, and real-time decision support, focusing on analytics, data engineering, and cross-functional communication using enterprise data systems.
Learning Outcomes
- Write and optimize SQL queries to extract business insights from relational databases.
- Create and manipulate complex joins, subqueries, CTEs, and window functions.
- Integrate SQL with Python, Excel, and BI tools for analytics and reporting.
- Prepare AI-ready datasets for predictive models using SQL logic.
- Translate raw data into actionable intelligence for strategic decisions.
SQL for AI-Driven Data Insights Syllabus T30
| Session No. | Topics | Tool/Reading/Activity | Skill Focus |
|---|---|---|---|
| 1 | Introduction to Databases and SQL | Beaulieu Ch.1 | Data Fluency |
| 2 | SELECT Statements and Filtering Data | Ch.2 | Query Fundamentals |
| 3 | Sorting, Aliases, and Calculated Columns | Ch.3 | Data Customization |
| 4 | SQL Functions (String, Numeric, Date) | Ch.4 | Business Logic |
| 5 | Joins: INNER, LEFT, RIGHT, FULL | Ch.5 | Relational Modeling |
| 6 | Advanced Joins and Query Optimization | Ch.6 | Efficiency & Scaling |
| 7 | GROUP BY, Aggregations & HAVING Clause | Ch.7 | Business Metrics |
| 8 | Subqueries and Nested Queries | Ch.8 | Analytical Reasoning |
| 9 | Common Table Expressions (CTEs) | Ch.9 | Query Structuring |
| 10 | Window Functions & Ranking | Ch.10 | Advanced Analytics |
| 11 | Data Cleaning and Transformation in SQL | Ch.11 | Preprocessing |
| 12 | Case-Based Business Queries | Hands-on Lab | Decision Support |
| 13 | SQL in Python Using Pandas & SQLAlchemy | Jupyter Lab | Integration Skills |
| 14 | Building AI Datasets with SQL Logic | Project-Based | AI Readiness |
| 15 | Data Modeling: ERDs, Keys, Normalization | Lecture + ERD Lab | Data Architecture |
| 16 | Views, Indexes, and Stored Procedures | Ch.12 | Database Efficiency |
| 17 | BI Dashboards: Connecting SQL to Power BI | Power BI Demo | Visualization |
| 18 | BigQuery and Cloud SQL | GCP Hands-on | Scalability & Cloud |
| 19 | Capstone Project: AI-Powered Data Insights | Team Work | Applied Problem Solving |
| 20 | Project Presentations & Review | Student Presentations | Strategic Communication |
Textbook & Resources
Primary Tools & Libraries:
- PostgreSQL / MySQL / SQLite / BigQuery
Reference Books:
- Learning SQL by Alan Beaulieu
- SQL for Data Analytics by Upom Malik, Matt Goldwasser, Benjamin Johnston