Streamlit Mastery for Data Scientists

Streamlit Mastery Final Project: Building an End-to-End Data Science Application

The final project requires students to build a fully functional, interactive data science web application using Streamlit. Follow these steps: 1. Dataset Selection: Choose a real-world dataset from a source like Kaggle or UCI. 2. Data Processing: Use Pandas to clean and transform the data directly within the app logic. 3. Interactive Dashboard: Implement at least three different types of interactive charts (e.g., Plotly, Altair, or Pydeck) that update dynamically based on user-selected filters. 4. State Management: Utilize st.session_state to track user preferences, login states, or multi-step processes. 5. Machine Learning Integration: Integrate a pre-trained model or train a Scikit-Learn model to provide real-time predictions based on user input. 6. Performance Optimization: Apply @st.cache_data and @st.cache_resource to optimize data loading and model inference. 7. Professional UI: Organize the application using columns, containers, tabs, and a sidebar for a polished user experience. 8. Deployment: Publish your application to Streamlit Community Cloud and provide the public URL.