The human brain excels at finding patterns in visual representations, which is why data visualizations are essential to any analysis. Done right, they bridge the gap between those analyzing the data and those consuming the analysis. However, learning to create impactful, aesthetically-pleasing visualizations can often be challenging. This session will equip you with the skills to make customized visualizations using Python.

Section 1: Getting Started With Matplotlib

While there are many plotting libraries to choose from, the prolific Matplotlib library is always a great place to start. Since various Python data science libraries utilize Matplotlib under the hood, familiarity with Matplotlib itself gives you the flexibility to fine tune the resulting visualizations (e.g., add annotations, animate, etc.). Moving beyond the default options, we will explore how to customize various aspects of our visualizations. Afterward, you will be able to generate plots using the Matplotlib API directly, as well as customize the plots that other libraries create for you.

Section 2: Moving Beyond Static Visualizations

While static visualizations are limited in how much information they can show, animations make it possible for our visualizations to tell a story through movement of the plot components (e.g., bars, points, lines), which can encode another dimension of the data. In this section, we will focus on creating animated visualizations before moving on to create interactive visualizations in the next section.

Section 3: Building Interactive Visualizations for Data Exploration

When exploring our data, interactive visualizations can provide the most value. Without having to create multiple iterations of the same plot, we can use mouse actions (e.g., click, hover, zoom, etc.) to explore different aspects and subsets of the data. In this section, we will learn how to use HoloViz to create interactive visualizations for exploring our data utilizing the Bokeh backend.