This beginner-friendly course introduces learners to Seaborn in Python, a powerful library built on Matplotlib for statistical data visualization. Designed with a structured, hands-on approach, the course guides learners from foundational relational plots to advanced categorical and statistical visualizations.

In Module 1, students will construct and interpret scatter plots, line plots, and faceted relational charts to analyze trends and relationships in data. Using Bloom’s Taxonomy verbs, learners will differentiate patterns, apply semantic mappings, and evaluate multi-variable relationships effectively. In Module 2, the focus shifts to categorical and statistical visualizations. Students will design and analyze boxplots, violin plots, barplots, countplots, swarmplots, stripplots, and catplots, gaining the ability to summarize distributions, measure central tendencies, and visualize confidence intervals with precision. By the end of this module, learners will be able to apply Seaborn’s figure-level functions to create meaningful, multi-faceted insights from categorical datasets. Through practice-based learning, quizzes, and structured lessons, learners will not only visualize data but also evaluate and communicate insights clearly, equipping them with essential data visualization skills in Python using Seaborn.