An interactive visualization library, Bokeh allows users to create diverse graphics and highly interactive dashboards and data applications. In this course, you'll achieve a foundational knowledge of using Bokeh to build simple graphs and visualizations. You'll start by exploring how to install Bokeh on your local machine, display charts inline within your Jupyter notebooks, and create an interactive visualization. You'll then recognize how to save Bokeh charts as HTML and PNG files. Next, you'll investigate how to visualize categorical data using bar charts, stacked bar charts, and clustered bar charts. You'll also identify how to implement pie charts and donut charts to represent compositions in your data. You'll finish the course by examining the ease of interactivity and granular customizations that Bokeh offers.

Bokeh facilitates the creation of high-performance charts allowing users to build impactful web-based dashboards and applications. In this course, you'll investigate how to visualize your data using complex charts in Bokeh. First, you'll identify how to visualize relationships that exist in your data using scatter plots, discover the function of jitter in viewing individual data points, and configure scatter plots where both axes represent continuous values. Next, you'll outline how to represent relationships between pairs of variables using heatmaps. You'll then recognize the use of line and area charts to visualize time-series data. Finally, you'll explore how to visualize data structures in the form of nodes and edges using network graphs. When you have completed this course, you'll possess the skills and knowledge to build simple as well as complex interactive visualizations using Bokeh.