Data visualization is how you turn numbers into something people can understand at a glance. A well-chosen chart reveals trends, comparisons, and outliers that a table of figures hides, and it is often the most persuasive part of an analysis.
In this module you'll learn to build charts in Python with Matplotlib and Seaborn: line, bar, scatter, and pie charts, histograms, and statistical plots such as box plots, violin plots, and heatmaps. You'll also learn how to customize titles, labels, colors, and styles so your visuals are clear, readable, and ready to share.
Once your charts are clear and effective, you'll be ready for the next module, BI Tools & Dashboards, where you bring visuals together into interactive dashboards.
Corey Schafer's introduction to Matplotlib: making simple line plots, customizing your graphs, and learning the basics of working with the library.
A 90-minute freeCodeCamp crash course on Matplotlib, showing how to build and style charts and apply them to a real-world dataset.
A crash course on Seaborn, the Python library built on top of Matplotlib that makes it simple to create attractive statistical charts.
A full walkthrough of Seaborn for data visualization in Python, covering the different chart types the library offers.
A 4-hour freeCodeCamp course that takes you from reading and cleaning data with NumPy and Pandas through to visualizing it with Matplotlib and Seaborn.