Build with AI: LLM-Powered Data Analysis App with Python and Streamlit
1h 26mIntermediate2025-09-12
Authors
Maggie Ma
Course details
In this quick, hands-on course, learn to build a lightweight, AI-powered data analysis tool using Python, Streamlit, and the OpenAI API. Instructor Maggie Ma shows you how to upload a dataset and ask questions in plain English—your app will convert the input into code (Python or SQL), run it, and return useful insights. Perfect for technical teams looking to support non-technical stakeholders, this course takes you through building and testing a natural language interface for structured data. No prior experience with LLMs is needed—just Python and a bit of pandas—and you’ll finish the course with a working prototype you can expand or deploy internally.
Learning objectives
Build a simple Streamlit interface that accepts CSV uploads.
Integrate the OpenAI API to answer data questions in natural language.
Design prompts that turn user input into code for data analysis.
Execute and return summaries or charts based on that generated code.
Test and share your app with stakeholders or teammates.
Learning objectives
Build a simple Streamlit interface that accepts CSV uploads.
Integrate the OpenAI API to answer data questions in natural language.
Design prompts that turn user input into code for data analysis.
Execute and return summaries or charts based on that generated code.
Test and share your app with stakeholders or teammates.
Skills covered
StreamlitSnowflakeGenerative AIArtificial Intelligence FoundationsPythonData AnalysisArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceOne-Off
Concepts
0. Introduction
- 01 - What you'll build - AI data analysis assistant
- 02 - App design fundamentals
1. Building the Streamlit App Foundation
- 03 - Create a basic Streamlit application
- 04 - Essential components - Sidebar and file uploader
- 05 - Display data tables and summary
- 06 - Build chatbot components
2. Connecting to OpenAI and Building Smart Prompts
- 07 - OpenAI API key security and setup
- 08 - Make your first API call
- 09 - Advanced prompt engineering
3. Code Generation and Execution
- 10 - Execute AI-generated Python code
- 11 - Robust code error handling
- 12 - Polish the user interface
4. Adding Memory and Advanced Features
- 13 - Build chat history and memory
- 14 - Optimize API cost and token usage
- 15 - Export conversation history
5. Wrap-Up and Next Steps
- 16 - Final app walk-through
- 17 - Deploy to Streamlit Cloud
- 18 - Next steps and extensions
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