This specialization equips learners with the skills to create, analyze, and customize data visualizations using Python’s Seaborn library. Starting from foundational plots, learners progress to advanced statistical and multivariate visualizations, mastering techniques for exploratory data analysis and storytelling. With hands-on coding practice, guided examples, and real datasets, participants gain practical expertise to communicate insights effectively. Designed for aspiring data analysts, scientists, and Python developers, the program blends data wrangling, visualization, and interpretation skills essential for data-driven decision making.
Seaborn Python Data Visualization & Analysis Specialization
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Seaborn Python Data Visualization & Analysis Specialization
Master Python Data Visualization with Seaborn. Build, customize, and analyze statistical data visualizations using Python Seaborn
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
Apply Seaborn to construct and customize statistical and categorical plots.
Analyze and interpret multivariate data relationships using advanced visualizations.
Communicate insights effectively through exploratory data analysis and storytelling.
Overview
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
September 2025
22 practice exercises
Advance your subject-matter expertise
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- 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 scatter, line, and faceted relational plots to analyze data trends.
Design and interpret categorical plots such as box, violin, and bar charts.
Apply Seaborn’s figure-level functions to create clear, multi-variable insights.
Skills you'll gain
Category: Descriptive Statistics
Category: Data Presentation
Category: Exploratory Data Analysis
Category: NumPy
Category: Plot (Graphics)
Category: Statistical Visualization
Category: Data Visualization Software
Category: Seaborn
Category: Statistical Analysis
Category: Pandas (Python Package)
Category: Box Plots
Category: Data Literacy
Category: Matplotlib
Category: Scatter Plots
What you'll learn
Analyze and visualize univariate and bivariate data distributions in Seaborn.
Build regression-based visualizations to model and interpret relationships.
Customize statistical plots using hue, facet grids, and styling for insights.
Skills you'll gain
Category: Descriptive Statistics
Category: Statistical Modeling
Category: Matplotlib
What you'll learn
Prepare and preprocess census datasets for exploratory data analysis.
Create scatter, violin, swarm, heatmap, and multivariate plots in Seaborn.
Enhance chart readability and interpret insights for data storytelling.
Skills you'll gain
Category: Seaborn
Category: Data Visualization Software
Category: Plot (Graphics)
Category: Statistical Visualization
Category: Data Storytelling
Category: Pandas (Python Package)
Category: Exploratory Data Analysis
Category: Scatter Plots
Category: Matplotlib
What you'll learn
Configure Python and Seaborn to prepare and explore census datasets.
Generate scatter, violin, heatmap, and multivariate visualizations.
Enhance plots for readability and interpret data insights effectively.
Skills you'll gain
Category: NumPy
Category: Data Processing
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
The Specialization can typically be completed within 6 to 8 weeks, with learners dedicating an average of 3–4 hours per week. This flexible schedule is designed to accommodate both working professionals and students, allowing learners to progress steadily through the courses while building strong, hands-on proficiency in data wrangling and visualization using Python’s Seaborn library. By the end of the program, participants will have gained practical, job-ready skills to create and interpret advanced data visualizations for real-world applications.
Learners should have a basic understanding of Python programming, including working with data structures and libraries such as Pandas. Prior exposure to statistics or data analysis concepts is helpful but not required.
Yes, it is recommended to take the courses in the suggested order. The curriculum is designed to build progressively—from foundational visualizations to advanced statistical analysis—ensuring learners develop a structured, step-by-step mastery of Seaborn.
More questions
Financial aid available,