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.
Matplotlib: Python Data Visualization & Wrangling Specialization
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Matplotlib: Python Data Visualization & Wrangling Specialization
Master Python Data Visualization with Matplotlib. Learn to build, customize, and optimize advanced Matplotlib visualizations with Python.
Instructor: EDUCBA
Included with 
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Create and customize high-quality data visualizations using Matplotlib.
Apply advanced plotting, styling, and layout techniques to complex datasets.
Translate raw data into actionable insights through real-world visualization projects.
Overview
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
September 2025
34 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from EDUCBA
Specialization - 4 course series
What you'll learn
Construct simple and multi-axis plots with labels, scaling, and annotations.
Design specialized charts including polar plots, streamplots, and pie charts.
Customize styles, axes, and figures to produce publication-ready visuals.
Skills you'll gain
Category: Matplotlib
Category: Data Storytelling
Category: Scientific Visualization
Category: Software Installation
Category: Scatter Plots
Category: Statistical Visualization
Category: Histogram
Category: NumPy
Category: Data Visualization
Category: Data Visualization Software
Category: Graphing
What you'll learn
Apply paths, transformations, and axes customization for precise control.
Use advanced colormaps, scaling, and annotations to enhance clarity.
Build 3D and specialized plots to communicate complex, multi-dimensional data.
Skills you'll gain
Category: Visualization (Computer Graphics)
Category: Scatter Plots
Category: Scientific Visualization
Category: NumPy
Category: Matplotlib
Category: Python Programming
Category: Statistical Visualization
Category: Data Visualization Software
Category: Plot (Graphics)
What you'll learn
Customize legends, styles, and figure layouts using GridSpec and property cycles.
Apply advanced subplot strategies with nested GridSpecs and constrained layouts.
Optimize plot structures with legends, colorbars, and annotations for clarity.
Skills you'll gain
Category: Matplotlib
Category: Scientific Visualization
Category: Data Presentation
Category: Data Visualization Software
Category: Plot (Graphics)
What you'll learn
Prepare and explore e-commerce datasets using Python and Matplotlib basics.
Create line, bar, scatter, pie, and box plots to analyze trends and insights.
Interpret patterns, distributions, and outliers for actionable business decisions.
Skills you'll gain
Category: Software Installation
Category: Exploratory Data Analysis
Category: Pandas (Python Package)
Category: Data Visualization Software
Category: Data Processing
Category: Histogram
Category: Box Plots
Category: E-Commerce
Category: Data Analysis
Category: Matplotlib
Category: Business Analytics
Category: Scatter Plots
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Frequently asked questions
The Specialization can typically be completed within 10 to 11 weeks, with a recommended commitment of 3–4 hours per week. This timeline allows learners to progress steadily through each course, building a strong foundation in Matplotlib before advancing to more complex visualization techniques and real-world case studies. The flexible structure ensures that learners can balance their studies with professional or academic commitments while still gaining the hands-on skills needed to create impactful, publication-ready data visualizations.
Learners should have a basic understanding of Python programming and familiarity with fundamental data analysis concepts. No prior experience with Matplotlib is required, as the Specialization begins with foundational skills before moving into advanced techniques.
Yes, the courses are designed to be taken sequentially. Each course builds on the concepts introduced in the previous one, progressing from core visualization techniques to advanced customization and applied case studies for real-world data storytelling.
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