Data Analytics with Python / Phase 6: Statistical Visualization with Seaborn

Phase 6 Overview - Statistical Visualization (Seaborn)

Seaborn phase landing page and lesson roadmap.

1 min readupdated 6 Oct 2026

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.

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.

Check your understanding

Three quick questions before you start the first lesson.

quizCheck yourself

Why do analysts often reach for seaborn instead of raw Matplotlib for exploratory analysis?
Show answer
B — Seaborn accepts a DataFrame plus column names and handles grouping and summaries for you — Seaborn is built on top of Matplotlib. Its value is the higher-level interface: pass data= and column names, and it groups, aggregates and adds things like confidence intervals for you.
You want to see how a single numeric column is spread out. Which family of plots fits best?
Show answer
B — Distribution plots such as histograms and KDE — Histograms and KDE plots describe one variable. Pair plots, joint plots and facet grids compare several variables, so they come later in the phase.
In which order does this phase teach the plots?
Show answer
B — From one-variable plots towards many-variables-at-once plots — The roadmap moves from distributions, to categorical comparisons, to relationships between two variables, and finally to multi-variable views such as pair plots and facet grids.

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