This hands-on course teaches learners how to prepare, analyze, and visually interpret data using Python’s Seaborn library, with a focus on census datasets. Beginning with foundational setup—such as installing Anaconda, configuring Jupyter Notebook, and loading libraries—the course progresses into exploratory data analysis and practical visualization techniques.
Seaborn Setup: Tools, Data Prep & EDA for Visualization
Seaborn Setup: Tools, Data Prep & EDA for Visualization
This course is part of Seaborn Python Data Visualization & Analysis Specialization
Instructor: EDUCBA
Access provided by University of Colombo
Gain insight into a topic and learn the fundamentals.
4 hours to complete
Flexible schedule
Learn at your own pace
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
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Assessments
7 assignments
Taught in English
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August 2025
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This course is part of the Seaborn Python Data Visualization & Analysis Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
This module introduces learners to the foundational setup required for performing data visualization using Seaborn on census datasets. It covers essential technical prerequisites including tool installation, library setup, environment management, and preliminary data preparation. Learners will install necessary software, configure a Python environment using Anaconda and Jupyter Notebook, and explore the structure and purpose of the dataset. The module also walks through the beginning stages of exploratory data analysis (EDA), including understanding data structures and manipulating datasets to prepare them for visualization in later modules.
What's included
5 videos1 reading3 assignments1 plugin
This module explores advanced data visualization techniques using Seaborn to analyze census data. Learners will apply core and advanced plotting tools to generate meaningful visual interpretations, manage axis readability, and derive statistical insights through categorical and continuous data relationships. The focus includes creating scatter plots, line plots, swarm plots, violin plots, point plots, heatmaps, and grid-based multivariate plots, with an emphasis on enhancing plot clarity and interpretability.
What's included
10 videos4 assignments
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