Master the art of data visualization with Python's Matplotlib library by learning how to create, customize, and evaluate clear, professional-quality charts. This course guides you from the fundamentals of plotting through advanced visualization techniques, helping you build the skills needed to communicate data effectively.
You will begin by configuring your Python environment, installing Matplotlib, and creating basic line plots while learning how to work with figures, axes, labels, scaling, and annotations. As you progress, you will explore advanced plotting techniques, including custom dashed lines, pseudocolor meshes, streamplots, ellipses, polar charts, pie charts, and logarithmic plots. You will also learn to customize figure styles, integrate image data, modify axes properties, and produce publication-ready visualizations with Matplotlib's styling tools.
Designed for learners who want to strengthen their Python data visualization skills, this course provides a structured learning path from foundational concepts to advanced customization. By the end of the course, you will be able to create context-specific visualizations, select appropriate chart types, refine plot appearance, and develop polished visual outputs that support effective data storytelling using Matplotlib.
This module introduces learners to the essential concepts and workflows of creating visualizations using Matplotlib. It covers the installation and setup of Python and Matplotlib, fundamental plotting commands, customization of simple plots, and managing figures and axes. Learners will develop the…
This module builds on foundational Matplotlib skills by exploring advanced chart types, specialized visuals, and customization techniques. Learners will work with complex plot elements such as custom line patterns, pseudocolor meshes, streamplots, ellipses, polar charts, and pie charts. They will a…
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EDUCBA is in Tier 5: mass-produced or unclear of our institution ranking (36/100). Thousands of courses, mostly slides and screen recordings.
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
Harvard University · edX
Learn the concepts and techniques that make up the foundation of data science and machine learning.
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
Harvard University · edX
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
Harvard University · edX
Learn to use machine learning in Python in this introductory course on artificial intelligence.
