Matplotlib: Python Data Visualization & Wrangling

EDUCBA via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

4 weeks, 10 hours a week

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Overview

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This Specialization equips learners with the skills to transform raw data into compelling, publication-ready visualizations using Python’s Matplotlib library. Starting with core plotting concepts, learners progress to advanced customization, 3D charts, and specialized layouts. Practical case studies, such as e-commerce data visualization, ensure hands-on application of concepts to real-world datasets. By the end, learners will be able to design and refine professional-grade visuals that communicate insights effectively for data science, business analytics, and research.

Syllabus

Course 1: Mastering Data Visualization with Matplotlib
Course 2: Advanced Data Visualization with Matplotlib Mastery
Course 3: Advanced Matplotlib: Design & Customize Visualizations
Course 4: Matplotlib with Python: E-commerce Data Visualization

Courses

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11 hours 21 minutes
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This advanced-level course equips learners with the skills to design, customize, and interpret complex data visualizations using Matplotlib. Through a structured progression from foundational customization techniques to specialized plotting methods, learners will explore paths, transformations, colors, colormaps, text rendering, annotations, axes customization, and 3D visualization. Starting with advanced path and transformation features, participants will apply precision control over plot structure and aesthetics. They will then evaluate and select optimal colormaps and scaling strategies to represent diverse datasets effectively. The course further enables learners to integrate advanced annotation techniques and Axes Artist functionalities to enhance plot clarity and context. In the final modules, learners will construct dynamic 3D plots and specialized visuals to communicate complex, multi-dimensional information. By the end of this course, learners will be able to create, modify, and optimize publication-quality visualizations tailored to their analytical needs, ensuring both accuracy and visual impact.
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5 hours 42 minutes
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This course empowers learners to move beyond basic plotting and construct advanced, professional-quality visualizations using Matplotlib. Through hands-on lessons, participants will apply customization techniques to legends, figure layouts, and subplot arrangements, and optimize complex visual structures for clarity and impact. In Module 1, learners develop skills in creating and customizing legends, applying property cycles, and refining figure layouts using GridSpec. They will manipulate legend handlers, design consistent styling systems, and integrate visual elements cohesively within a figure. In Module 2, learners implement advanced layout strategies, including nested GridSpecs, constrained layout, and tight layout adjustments. They will control padding and spacing, incorporate global figure legends, and adapt plot arrangements dynamically to accommodate annotations, colorbars, and changing figure sizes. By the end of the course, learners will be able to design, arrange, and fine-tune complex plot structures, ensuring that every element serves the dual purpose of aesthetic appeal and effective data communication.
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7 hours 20 minutes
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This comprehensive course equips learners with the skills to create, customize, and evaluate high-quality visualizations using Python’s Matplotlib library. Beginning with foundational plotting concepts, learners will identify key Matplotlib components, construct simple and multi-axis plots, and apply labeling, scaling, and annotation techniques to effectively convey data insights. In the advanced modules, learners will design and differentiate specialized charts, including custom dashed lines, pseudocolor meshes, streamplots, ellipses, polar charts, and pie charts. They will manipulate figure styles, integrate image data, and modify axes properties to produce publication-ready visuals. By the end of the course, learners will be able to synthesize plotting techniques to create professional, context-specific visualizations that enhance data-driven storytelling.
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5 hours 30 minutes
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This course provides a practical, hands-on case study in e-commerce data analysis using Matplotlib with Python. Designed at the intermediate level, it equips learners with the skills to set up the environment, explore datasets, and construct compelling visualizations that reveal meaningful business insights. In Module 1, learners will demonstrate the installation of Anaconda and Matplotlib, analyze e-commerce datasets by identifying unique values and preparing data, and apply visualization basics by working with figures, axes, and plotting approaches. In Module 2, learners will construct line graphs and histograms to interpret trends and data distributions, apply bar graphs and scatter plots to compare categories and evaluate relationships between variables, and create pie charts and boxplots to assess proportions, quartiles, and outliers for statistical insights. By the end of this course, learners will be able to analyze raw e-commerce datasets, apply Matplotlib techniques to construct visualizations, and evaluate patterns for actionable insights — essential skills for anyone pursuing data science, business analytics, or Python visualization projects.