Curriculum

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    Chapter 1: Introduction to Streamlit

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    Chapter 2: Setting Up the Development Environment

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    Chapter 3: Creating and Deploying Your First Streamlit App

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    Chapter 4: Exploring Streamlit’s Flow and Architecture

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    Chapter 5: Persisting Data and State Across App Reruns

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    Chapter 6: Exploring Streamlit’s Page Elements

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    Chapter 7: Widget Keys and Callbacks

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    Chapter 8: Introduction to Streamlit Caching and Connections

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    Chapter 9: Managing Secrets in Streamlit

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    Chapter 10: Advanced App Management Concepts

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    Chapter 11: App Configuration Options

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    Chapter 12: Building Multipage Streamlit Apps

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    Chapter 13: Testing Streamlit Apps

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    Chapter 14: Building a Data Analysis Streamlit App

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    Chapter 15: Building a Machine Learning Streamlit App

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    Chapter 16: Building a Chatbot on Streamlit

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About the Course

Streamlit has transformed how developers present data science and machine-learning work by making it effortless to turn Python scripts into interactive web applications. Building Data Apps with Streamlit provides a complete, hands-on roadmap to creating professional, production-ready apps using Streamlit’s fast, intuitive, and Pythonic framework. You begin with Streamlit’s architecture, layout system, and component ecosystem, learning how to build clean, scalable apps with widgets, callbacks, caching, and session state. The book then guides you through handling secrets, managing configurations, working with APIs and databases, and building multipage workflows that feel polished and responsive. By the end, you will build a full Streamlit solution that analyzes datasets, trains machine-learning models, and powers an AI chatbot using Google Gemini. With dedicated chapters on testing, optimization, and cloud deployment, this book equips you with the confidence and skills to create, iterate, and share high-quality Streamlit applications that bring your data and ideas to life.

About the Author

Siddhant Sadangi is a Developer Experience Engineer at neptune.ai, focusing on Python, MLOps, and Streamlit. A recognized Streamlit Creator and Community Moderator, he builds open-source tools and guides developers in turning data science projects into interactive web apps.