Python Data Visualization for the Web with Bokeh

Data visualization is essential for communicating insights, and tools like Bokeh allow you to create powerful, interactive charts directly usable on the web. This course teaches you the foundational steps needed to transform raw data into dynamic, beautiful plots using Python and the Bokeh library. By the end, you will be able to configure a clean development environment, generate various plot types, and reliably export your visualizations for sharing as static HTML pages. ### What you'll learn * Understand the core concepts of Bokeh, including glyphs, renderers, and the ColumnDataSource data structure. * Configure your Python development environment and manage dependencies for reliable visualization projects. * Practice creating common statistical plots like scatter plots, line charts, and bar charts using the Bokeh plotting interface. * Implement basic interactive features such as tooltips, hovering, and zooming to enhance user engagement. * Apply methods for styling your visualizations and saving the final output as a standalone, shareable static HTML file. The course begins with setting up your tools and exploring core Bokeh components. We then move into practical exercises creating various figure types before concluding with methods for effective output and sharing. This course is designed for absolute beginners in data visualization or Python users looking to start creating web-ready plots. No prior experience with Bokeh or advanced web technologies is required. Start building your interactive data portfolio today.