Develop Web Apps with Streamlit Course Syllabus

Overview: This course provides a hands-on introduction to building interactive, data-driven web applications using the Streamlit Python framework. Over approximately 13 hours, learners will progress from setting up their first app to deploying real-world projects like dashboards and API-powered tools. Each module combines concise theory with practical exercises, enabling rapid skill development without requiring any front-end experience.

Module 1: Introduction to Streamlit

Estimated time: 1.5 hours

• What is Streamlit
• Installation and setup
• Key features of Streamlit
• Building and launching a 'Hello World' app

Module 2: Layout and Widgets

Estimated time: 2 hours

• Text elements and formatting
• Using columns for layout design
• Adding interactive widgets: sliders, buttons, radio buttons
• Organizing components with the sidebar

Module 3: Working with Data

Estimated time: 2.5 hours

• Displaying pandas dataframes
• Creating charts with matplotlib, seaborn, and Plotly
• Performing data transformations in Streamlit
• Visualizing real datasets in live web apps

Module 4: Building Real Apps

Estimated time: 3 hours

• Developing a data explorer application
• Creating interactive dashboards
• Integrating external APIs into apps
• Building tools such as a stock visualizer and sentiment analyzer

Module 5: State Management and Forms

Estimated time: 2 hours

• Understanding st.session_state
• Handling form inputs
• Storing and retrieving user preferences
• Implementing multi-page logic

Module 6: Deployment and Sharing

Estimated time: 2 hours

• Deploying apps using Streamlit Community Cloud
• Integrating with GitHub
• Managing secrets and environment variables
• Sharing deployed applications with others

Prerequisites

• Basic knowledge of Python programming
• Familiarity with pandas for data manipulation
• Understanding of fundamental data structures and functions

What You'll Be Able to Do After

• Build interactive, data-driven web apps using Streamlit
• Create dashboards with charts, maps, and user inputs
• Integrate data from pandas, NumPy, and external APIs
• Deploy Streamlit applications to the cloud
• Customize app appearance and interactivity without front-end code