In this lesson, you'll get a more thorough overview of data visualization, Python's libraries, and how to create successful visualizations.

This section takes for granted having gone through the Data Visualization lab in the Python section well enough that you're familiar with how to create graphs in Matplotlib and alter their attributes, such as labels for axes.

If you need to do so, go back over that lesson again!

In this section, we're going to focus on Data Visualization. We'll begin by getting some deliberate practice in with some of Matplotlib's more advanced features, such as subplots.

Then, we'll dig into some of the best practices for data visualization, and learn how we can make visualizations that will look great in any professional environment. We'll also dig into some of the most common mistakes people make when creating visualizations, so that we can avoid them.

Finally, we'll learn about a more advanced visualization library, Seaborn. We'll get some practice with Seaborn, and get a feel for when to use Seaborn, and when to use Matplotlib.

You're ready to start exploring how to best visualize your data. Let's get started with this section!