Mastering Streamlit | Hands-on

Kick off the course by learning what Streamlit is and why it's useful, set up your environment, and build your first simple app while exploring the rerun model.

Understand what Streamlit is and why it’s popular for beginners and data professionals, as it converts Python scripts into interactive web apps with rapid development and no front-end work.

learn how Streamlet runs a script from top to bottom, rendering output in the browser, and how a full rerun occurs on code changes or user actions.

Understand what Streamlit is and why it's popular, set up a development environment, install and run your first app, and learn the rerun model that keeps the interface in sync.

Explore how to display content in a Streamlit app using core display elements. Learn to show text, tabular data, images, audio, video, metrics, and JSON with simple Python code.

Master Streamlit's content display by using text, tabular data, and media such as images and videos, via st.title, st.header, st.write, st.dataframe, st.table, st.image, st.metric, and st.json.

Explore Streamlit display elements—text, data, and media—with st.title, st.header, st.markdown, st.dataframe, st.table, st.image, st.audio, st.video, and structured outputs like st.metric and st.json, and preview interactive widgets in the next section.

Explore interactive widgets in a Streamlet app, from basic input widgets to advanced controls, and build a structured form that accepts user input and responds in real time.

Discover how Streamlit widgets drive app reactivity with reruns, and use buttons, checkboxes, text inputs, sliders, select boxes, file uploaders, and forms to connect user actions to Python logic.

Explore core layout and styling concepts in streamlit, structure apps with columns, containers, expanders, and the sidebar, then enhance UI with tabs, themes, and configuration to create clean, professional interfaces.

Practice building Streamlit layouts by creating columns, containers, and expanders, and configuring a sidebar with buttons and a select box to manage content and navigation.

Master layouts and styling in Streamlit by using st.columns, st.container, st.expander, st.sidebar, and st.tabs to structure apps, and configure visual identity with st.setpageconfig and config.toml, including charts and Plotly.

Explore charts and data visualization in Streamlit, build line, bar, area, and scatter charts, extend visuals with Plotly and geographic maps, and present raw data as interactive visuals.

Create quick visualizations in Streamlit with built-in charts like st.line_chart, st.bar_chart, and st.area_chart to display time series trends, then enhance interactivity with st.plotly_chart and st.map.

Build and customize built-in Streamlit charts using a sample dataframe with three columns, creating line, bar, area, and scatter charts, with color adjustments and interactive features.

Explore advanced visuals in Streamlit by building interactive Plotly charts and maps. Install Plotly, render with st.plotly_chart and st.map, and explore features like zoom, pan, and data points.

Explore streamlit built in charts for quick visualizations and leverage plotly for interactive, customizable charts. Apply st.map to plot geographic data, and prepare session state for caching and performance.

Master app state and performance in streamlet apps by persisting data with session state and caching with cache-data and cache-resource. Use command line tools to build reliable, fast, production-ready apps.

Streamlit apps rerun on every interaction, forgetting prior inputs; use st.session_state to preserve data across reruns. Improve performance with st.cache_data and st.cache_resource to avoid repeated heavy computations.

Demonstrates how st.session_state preserves values across reruns by replacing a normal counter with a smart counter, initializes state, and updates it via a button-driven callback.

Explore caching in Streamlit with st.cache_data and st.cache_resource, see how caching speeds up data loading and heavy resources, and learn how to manage cache.

Discover command line essentials for Streamlit: run apps with custom ports, clear cache, and use key tools like Streamlit run, config show, hello, help, and init.

Learn how to implement multi-page Streamlit apps with a folder structure and shared state across pages. Add a custom sidebar navigation to unify the experience and structure larger Streamlit projects.

Build a multi-page streamlit app with shared session state by organizing pages, configuring an entry point, and adding interactive home and courses pages with navigation, category filters, and enroll confirmations.

Explore how to integrate Streamlit's core building blocks into a complete data explorer app, from file upload and data preview to filters, analytics, and a multi-page dashboard.

Build a multi-page data explorer dashboard in Streamlit by uploading a CSV, processing data, and exploring AI adoption across countries through overview, workforce insights, and tools analysis.

Learn to build a multi-page Streamlit app with a file upload and data preview module, including a csv data loader, dataset listing, and interactive charts.

Organize a structured Streamlit project with folders, a file uploader, cached data loading, and three analytics pages: overview, workforce insights, and AI tools analysis; access codebase in the GitHub repository.

Explore how API calls work inside a Streamlit app and securely manage credentials with secrets.tml. Learn to fetch data from API endpoints using requests, parse JSON, and cache calls.

Learn to securely manage secrets in streamlit with a secrets.toml file in the .streamlit folder, accessed via st.secrets, avoiding unencrypted git storage and using api keys and db credentials.

Demonstrates setting up a Google Gemini API key, storing it in secrets.tml, installing the google-generative-ai SDK, and making a basic API call from a Streamlit app with st.secrets.

Learn to use APIs and secrets in a Streamlit app with st.secrets, caching, and buttons for performance, then build an AI chat assistant with the Gemini API for the capstone.