Streamlit
Streamlit is a free, non-proprietary tool that enables rapid development of machine learning and data science web…
- 25
- 45h
- 0
Streamlit Training:
Streamlit is a simple and powerful Python tool for creating interactive web apps with just a few lines of code. It is designed for data scientists, analysts, and developers who want to build apps quickly and easily without needing web development skills. With Streamlit, you can visualize data, create dashboards, and display machine learning models effortlessly. It supports popular Python libraries like Pandas, Matplotlib, and Scikit-learn, making it great for data analysis and AI applications.
You can add buttons, sliders, charts, and tables to make your app interactive. The app updates automatically when you change the code, making development fast and without interruptions.Streamlit is used in finance, healthcare, research, and business analytics for data-driven decision-making. It also allows easy cloud deployment, so you can share your apps instantly. With its simplicity and flexibility, Streamlit is the perfect tool for anyone who wants to build interactive Python apps with ease.
This Streamlit online course will guide you through building Streamlit apps from scratch, integrating data sources, creating dynamic visualizations, and deploying apps for real-world use. Whether you’re a data scientist, analyst, or developer, Streamlit makes web development easy without requiring extensive frontend knowledge. This Streamlit training will help you gain hands-on experience in creating interactive applications.By the end of the Streamlit course, you’ll be able to build and deploy interactive data-driven applications with ease!
What Will You Learn?
- Module 1: Introduction to Streamlit: What is Streamlit and Why Use It?, How Streamlit Simplifies Web App Development, Setting Up the Development Environment, Installing Streamlit and Running Your First App
- Module 2: Streamlit Basics – Building Your First App: Understanding Streamlit’s Workflow, Adding Text, Titles, and Formatting, Using Widgets: Buttons, Sliders, Checkboxes, and Text Inputs, Creating Interactive Elements for User Input
- Module 3: Data Handling in Streamlit: Loading and Displaying Data (CSV, JSON, Databases), Data Manipulation with Pandas and NumPy, Creating Dynamic Tables and DataFrames, Filtering and Sorting Data in Apps
- Module 4: Data Visualization with Streamlit: Introduction to Visualization Libraries (Matplotlib, Seaborn, Plotly), Creating Line, Bar, and Pie Charts in Streamlit, Interactive Charts with Altair and Plotly, Real-Time Data Updates and Dynamic Visualizations
- Module 5: Interactive Forms & User Input Handling: Accepting User Input through Forms, Handling File Uploads and Processing Files, Using Sidebar for Navigation and Better UI, Creating a Multi-Page App in Streamlit
- Module 6: Integrating Machine Learning Models: Loading and Deploying ML Models in Streamlit, Integrating with Scikit-learn, TensorFlow, and PyTorch, Displaying Predictions in an Interactive UI, Using Model Explanations with SHAP and Lime
- Module 7: Deploying Streamlit Apps: Introduction to Deployment Options, Deploying on Streamlit Cloud (Step-by-Step Guide), Hosting on AWS, GCP, and Heroku, Sharing Apps with Users & Managing Access
- Module 8: Advanced Features & Performance Optimization: Caching Data for Faster Performance, Using Session State for User Interactions, Running Background Tasks in Streamlit, Best Practices for Optimizing Streamlit Apps
- Module 9: Hands-on Projects & Real-World Applications: Building a Data Dashboard for Business Analytics, Creating an AI-Powered App with Machine Learning Predictions, Developing a Financial Data Tracker, Building a Real-Time Weather App
Course Curriculum
Module 1: Introduction to Streamlit for Data Engineering
Module 2: Building Interactive Data Applications
Module 3: Integrating with Kafka
Module 4: Leveraging Snowflake for Data Warehousing
Module 5: Cloud Integration and Deployment
Module 6: Advanced Topics and Real-World Projects
