Data Management & SQL (AI-Assisted)

Data management is at the heart of modern organizations, enabling businesses to store, process, and analyze information effectively. SQL (Structured Query Language) remains the industry standard for interacting with relational databases, allowing analysts to retrieve, filter, and aggregate data. In today’s AI-augmented environment, the ability to combine SQL skills with AI-powered query generation and verification tools can significantly accelerate the process of writing correct and efficient queries.

Objectives

By the end of this module, students will be able to:

• Write SELECT, WHERE, GROUP BY, and JOIN queries to retrieve and combine data.
• Use AI to draft SQL queries and create test cases to validate correctness.
• Discuss issues of data privacy and personally identifiable information (PII) in database query scenarios.

Lecture & Discussion

The session begins with an overview of relational database modeling, focusing on how entities, attributes, and relationships are structured. Students will review the importance of primary and foreign keys, as well as normalization practices that reduce redundancy and improve consistency. Query performance considerations will be introduced, highlighting how indexes and execution plans influence efficiency. Beyond technical execution, students will explore the ethical and legal dimensions of querying sensitive data, particularly when dealing with PII such as customer names, addresses, and payment details.

Hands-On Exercise

Students will work with a Sales Orders dataset to practice AI-assisted SQL. Using natural language prompts, they will generate and refine queries with AI support, learning how to critically evaluate AI-generated results. Exercises include filtering orders by date ranges, grouping sales by region, and joining tables such as Customers, Orders, and Products. Students will also build unit tests—small query checks that validate expected results—to ensure correctness and reliability. Debugging exercises reinforce the habit of not only trusting AI outputs, but also verifying them against sample data and test cases.

Lesson Summary

Data management is pivotal for modern organizations, facilitating the storage, processing, and analysis of information. SQL (Structured Query Language) stands as the industry standard for interacting with relational databases, empowering analysts to retrieve, filter, and aggregate data effectively. In today's AI-enhanced landscape, combining SQL proficiency with AI-driven query tools can significantly boost the speed and accuracy of crafting queries.

Objectives:

• Write SELECT, WHERE, GROUP BY, and JOIN queries to retrieve and merge data.
• Utilize AI for drafting SQL queries and devising test cases for validation.
• Delve into data privacy and handling personally identifiable information (PII) in database queries.

Lecture & Discussion:

The module kicks off with a relational database modeling overview, highlighting the structuring of entities, attributes, and relationships. Focus is placed on the significance of primary and foreign keys, along with normalization practices to enhance consistency. The session delves into query performance considerations, shedding light on how indexes and execution plans impact efficiency. Moreover, ethical and legal aspects of querying sensitive data, especially PII like customer details, are explored.

Hands-On Exercise:

Students will engage with a Sales Orders dataset for AI-assisted SQL practice. Through natural language prompts, they will collaborate with AI to generate and revise queries, learning to assess AI-generated outcomes critically. Exercises involve tasks such as filtering orders by date ranges, grouping sales by region, and joining tables like Customers, Orders, and Products. Additionally, students will devise unit tests—small query validations ensuring accuracy and dependability. Debugging exercises reinforce the importance of not solely relying on AI outputs but verifying them against sample data and test cases.