Data Visualization and Analysis With Seaborn Library Course Syllabus

Overview: This beginner-friendly course provides a hands-on introduction to data visualization using the Seaborn library in Python. Over approximately 9 hours of content, you'll learn to create insightful, publication-quality plots for real-world data analysis. The curriculum progresses from basic setup to advanced customization and ends with a practical exploratory data analysis workflow. Each module combines concise theory with interactive coding exercises using real datasets like 'tips' and 'iris', ensuring immediate application of skills.

Module 1: Introduction to Seaborn & Setup

Estimated time: 1 hour

• Seaborn installation and environment setup
• Understanding Seaborn's design philosophy
• Comparison with Matplotlib
• Load built-in datasets and create first plots

Module 2: Distribution Plots

Estimated time: 1.5 hours

• Create histograms and KDE plots
• Generate rug plots for data distribution
• Visualize joint distributions with jointplot
• Overlay KDE and histogram on univariate data

Module 3: Relational Plots

Estimated time: 1.5 hours

• Build scatter plots using relplot
• Create line plots with lineplot
• Visualize trends over time
• Use hue and style parameters for multivariate data

Module 4: Categorical Plots

Estimated time: 2 hours

• Generate bar, count, and box plots
• Create violin, strip, and swarm plots
• Compare categories across variables
• Customize order, orientation, and grouping

Module 5: Matrix Plots & Heatmaps

Estimated time: 1 hour

• Construct heatmaps for correlation matrices
• Generate cluster maps for pattern detection
• Display annotated heatmaps with custom palettes

Module 6: Styling & Customization

Estimated time: 1 hour

• Apply Seaborn themes (darkgrid, whitegrid)
• Adjust context settings (talk, poster)
• Customize color palettes
• Integrate with Matplotlib for fine-tuning

Module 7: Exploratory Data Analysis Workflow

Estimated time: 1.5 hours

• Combine multiple plot types for EDA
• Use facet grids and pair plots
• Conduct mini-EDA on real dataset
• Summarize findings with visual outputs

Prerequisites

• Basic familiarity with Python programming
• Fundamental understanding of Pandas library
• Basic knowledge of data analysis concepts

What You'll Be Able to Do After

• Understand when to use different Seaborn plot types
• Create publication-quality statistical visualizations
• Customize themes, colors, and layouts effectively
• Integrate Seaborn with Pandas and Matplotlib
• Perform end-to-end exploratory data analysis using visuals