Phase 6 Overview - Statistical Visualization (Seaborn)

What you’ll learn in Phase 6

Seaborn is a high-level plotting library built on Matplotlib, great for EDA and statistical charts.

In this phase you’ll learn:

• Distributions: hist, KDE
• Categorical comparisons: box, violin, count, bar
• Relationships: regression, joint plots
• Multi-variable exploration: pair plots
• Small multiples: facet grids

How the phase is organized

Each group of lessons builds on the one before it — start with the foundations, then move from “one variable” plots toward “many variables at once” plots:

diagram Seaborn phase roadmap mermaid

  flowchart TD
  A["Foundations
(Intro to Seaborn, Seaborn vs Matplotlib)"] --> B["Distributions
(histplot / displot, KDE)"]
  B --> C["Categorical comparisons
(box, violin, count, bar)"]
  C --> D["Relationships
(regplot / lmplot, joint plots)"]
  D --> E["Multi-variable views
(pair plots, facet grids)"]

Lessons progress from foundations to distributions, categorical comparisons, relationships, and finally multi-variable/faceted views.

From the book

In Python for Data Analysis, Wes McKinney introduces seaborn as the library you reach for once plotting starts to need aggregation — like averaging tip_pct per day, or a confidence interval on a bar. Doing that by hand in Matplotlib takes several lines; seaborn functions take a data= DataFrame and column names, and handle the grouping and math for you.

Recommended order

Follow the sidebar order for a smooth learning path — it mirrors the roadmap above, moving from single-variable distributions to full multi-variable exploration.

Next

Start with Introduction to Seaborn to install the library and make your first plot.