Building a Simple Data Analyst AI Agent with Llama and Flask

Bridge the gap between data analysis and AI orchestration. This practical course, "Building a Simple Data Analyst AI Agent with Llama and Flask" created by Kiril Spiridonov, offers a comprehensive blueprint for developers and data professionals looking to leverage local Large Language Models (LLMs) for real-world business intelligence. Students will move beyond basic API queries to deploy the open-source Llama model locally, minimizing reliance on expensive cloud providers and maintaining data privacy. The curriculum breaks down advanced prompt engineering paradigms such as In-Context Learning (ICL), Chain of Thought (CoT), and Tree of Thought (ToT) to structure complex logical reasoning. Through hands-on exercises, learners build a Flask web application that connects seamlessly with a PostgreSQL database. By mastering targeted prompt design, you will teach your AI agent to translate natural language user questions into precise SQL queries, execute them safely, and synthesize the results back into plain English. This course is ideal for software engineers, data analysts, and backend developers wanting to build cost-effective, secure, and private AI agents without paid API dependencies.