Data Science Portfolio: Kaggle Dataset to Streamlit App

Standing out in the data science field requires more than just knowing how to train a model; you need to demonstrate your ability to build and share working applications. This text-based course guides you through creating a complete data science portfolio project from scratch. You will start with raw Kaggle dataset files and transform them into a deployed, interactive web application using Python and Streamlit. Along the way, you will learn essential modern development practices, including setting up virtual environments and writing clean, maintainable code. What you'll learn: - Understand the foundational concepts of data processing and machine learning workflows. - Process and analyze datasets using modern Python data manipulation techniques. - Build interactive web applications using the Streamlit framework. - Manage project dependencies with modern virtual environment tools. - Deploy your finished application to the web so others can interact with it. - Write clean, readable code with type hints to ensure professional quality. The course begins with an introduction to basic data terminology and environment setup before moving into hands-on data cleaning, model building, and application development. You will follow step-by-step written instructions and code snippets to assemble your project piece by piece. This course is designed for absolute beginners and aspiring data professionals with no prior deployment experience. Start building your interactive data science portfolio today.