Matplotlib Tutorials by Corey Schafer

About this course

Turn raw numbers into visuals people understand. This free online course helps you build practical Matplotlib skills in Python so you can present data clearly, support decisions, and communicate insights with confidence. Whether you are learning programming, moving into data analysis, or improving your reporting workflow, Matplotlib is a core tool that keeps showing up in real projects because it is flexible, widely used, and works well with common data formats.

You will start by creating and customizing your first plots, learning how to control titles, labels, legends, colors, and styling so your charts look intentional instead of default. From there, you will practice selecting the right visual for the question you are trying to answer: comparisons with bar charts, proportions with pie charts when appropriate, trends with line charts, distributions with histograms, and relationships with scatter plots. You will also work with data coming from CSV files, reinforcing the everyday task of moving from a dataset to a meaningful graphic.

As you progress, you will strengthen the kind of understanding that makes your charts more than just pictures. You will learn to interpret distributions and communicate them well, choose binning strategies for histograms, and highlight changes over time using area fills and stack plots. The course also covers plotting time series data with readable date labels, a key skill for analytics dashboards and reports.

To bring everything together, you will explore real-time plotting concepts and how live charts can help you monitor changing values during experiments or ongoing processes. Finally, you will learn to compose multiple charts in a single figure using subplots, making it easier to tell a complete story and compare views side by side. With short lessons and practice questions throughout, you will build a solid foundation to create polished Python visualizations for coursework, portfolios, and professional use.