This intermediate-level course is designed to help learners analyze, visualize, and interpret data distributions using the powerful Seaborn library in Python. Building upon foundational knowledge of data visualization, the course takes a hands-on approach to explore univariate and bivariate distributions, apply linear and polynomial regression models, and demonstrate advanced statistical plots such as KDE plots, pairplots, jointplots, and lmplots.

Through structured lessons and guided coding examples, learners will gain practical experience in crafting insightful visualizations that enhance exploratory data analysis. Emphasis is placed on understanding the relationship between variables and how these relationships can be effectively communicated using Seaborn’s built-in functions. By the end of the course, learners will be able to: • Identify key distribution types and the appropriate plots to represent them. • Construct regression-based visualizations to model complex relationships. • Customize multivariate visualizations using hue, facet grids, and plot styling. • Evaluate patterns and trends in data using statistical plotting techniques. This course is ideal for aspiring data analysts, data scientists, and Python developers looking to advance their data storytelling and statistical graphics capabilities using Seaborn.