Interactive Data Visualization with Bokeh and Pandas

Data is only as powerful as the story it tells, but static charts often fail to capture the deep insights hidden within complex datasets. Interactive visualizations allow users to explore data dynamically, making your analysis far more engaging and impactful. This course teaches you how to transform raw data into interactive web plots using Bokeh and Pandas. You will start with foundational data visualization concepts and essential terminology before moving on to practical data handling. You will learn how to source public datasets, clean and import CSV files using Pandas, and structure your data for visualization. From there, you will progress to building interactive scatter plots, line charts, and bar graphs that users can filter and hover over for real-time details. What you'll learn: - Understand the core principles of interactive data visualization and Bokeh architecture - Import and clean public datasets from sources like Kaggle using Pandas - Create dynamic, web-ready plots, charts, and layouts with custom styling - Implement interactive tools such as hovers, pans, and zooms to enhance user exploration - Structure your data processing pipeline to feed clean data directly into Bokeh figures This text-based course guides you step-by-step from raw CSV files to fully interactive web plots, reinforcing each concept with clear code explanations and written exercises. You will build a solid workflow for preparing and presenting data visually. This course is designed for beginners, aspiring data analysts, and Python developers who want to expand their data visualization skills. No prior experience with Bokeh or data visualization libraries is required, though a basic familiarity with Python variables is helpful. Start learning today and bring your data to life.