Introduction to Text-to-SQL with LLMs for Data Analytics

As data grows more complex, the ability to query databases using plain English is transforming analytics. Integrating Large Language Models (LLMs) with SQL databases allows you to extract insights faster and build intelligent data assistants. This course teaches you how to bridge the gap between human language and database queries. You will explore how to design prompts that generate accurate SQL, provide database schema context to AI, and understand the foundational architecture of Text-to-SQL systems. What you will learn: Understand the core concepts of Large Language Models and their role in data analytics. Learn fundamental prompt engineering techniques to generate precise and secure SQL queries. Apply basic Retrieval-Augmented Generation (RAG) patterns to feed database schemas to your AI. Practice writing and refining natural language prompts to handle complex table joins and aggregations. Explore modern workflows for validating and executing AI-generated queries safely. The course begins with essential AI and database terminology, ensuring a solid foundation. From there, you will work through written examples and step-by-step text exercises that demonstrate how to construct effective Text-to-SQL pipelines and integrate them into analytical workflows. Designed for beginners, data analysts, and business intelligence enthusiasts, this course requires no prior advanced programming experience—just a basic understanding of what a database is. Start reading today to unlock the power of AI-driven data analytics and simplify your database queries.