Data Visualization with Matplotlib and Seaborn

This is a hands-on, project-based course designed to help you learn two of the most popular Python packages for data visualization: Matplotlib and Seaborn.

Get a quick introduction to data visualization frameworks and best practices, and review essential visuals, common errors, and tips for effective communication and storytelling.

Explore Matplotlib fundamentals and building and customizing line charts, bar charts, pies and donuts, scatterplots, histograms, and more. Break down the components of a Matplotlib figure, apply common chart formatting techniques, and leverage more advanced customization options like subplots, GridSpec, style sheets, and parameters. Finally, learn the basics of building charts and using visuals in the Seaborn library.

Throughout the course, you'll play the role of a consultant at Maven Consulting Group, where you can practice applying your skills to a range of real-world projects and case studies.

Learning objectives
Identify key data visualization best practices, including essential visuals and their use cases, common errors, and tips for formatting and effective storytelling.
Interpret Python syntax for creating chart objects with the Matplotlib library, including the PyPlot API and object-oriented interfaces.
Utilize the components of a Matplotlib chart object, including titles, legends, colors, styles, annotations, axis ticks, subplots, and GridSpecs.
Analyze Matplotlib plotting functions for different chart types, including line charts, bar charts, pie charts, histograms, and scatterplots.
Analyze Seaborn plotting functions for different chart types, including bar charts, histograms, box plots, violin plots, heatmaps, and linear relationship plots.