MODULE 5 OF 8 · 20 Hrs · 2 Weeks

Data Visualization (Matplotlib / Seaborn)

Analysis only creates value once it's understood by someone else. This module builds the skill to turn raw findings into clear, compelling visuals — from chart fundamentals through multi-panel dashboards.

Who This Module Is For

Students who have completed the EDA module and are ready to communicate findings visually.

Real-World Relevance

The ability to choose the right chart and present it clearly is what turns a data scientist's analysis into a decision — a skill tested directly in portfolio reviews and take-home assessments.

Program Overview View Hands-On Labs

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Curriculum

What You Will Learn

A detailed, industry-aligned breakdown of every topic covered in this module.

Principles of effective data visualization
Line, bar, scatter & histogram plots
Matplotlib figure & axes customization
Seaborn statistical plots & heatmaps
Multi-panel & faceted visualizations
Storytelling with data
Choosing the right chart for the data
Building simple visualization dashboards

Technology Stack

Tools You Will Use

Hands-on time with the same tools used by working data analysts and data scientists today.

Matplotlib

Core Python plotting library used to build line, bar, scatter and histogram visualizations.

Seaborn

Statistical visualization library built on Matplotlib, used for heatmaps and distribution plots.

Plotly

Interactive charting library used to build explorable, presentation-ready visualizations.

Practical Work

Hands-On Labs

Production-style data science lab scenarios, built using real, messy datasets.

Build line, bar, scatter and histogram plots for a real dataset using Matplotlib.

Create statistical plots and heatmaps using Seaborn.

Design a multi-panel, faceted visualization comparing multiple variables.

Choose and justify the right chart type for a given analytical question.

Assemble a small visualization dashboard that tells a coherent data story.

Evaluation

Assessment

Knowledge Assessment

Quiz covering chart-type selection, Matplotlib/Seaborn syntax and visualization best practices.

Practical Evaluation

Students must build a set of publication-quality visualizations that tell a clear, coherent story from a real dataset.

Portfolio

Projects

Industry-style deliverables added directly to your project portfolio.

Portfolio Project 01

Data Visualization Portfolio

Build a set of publication-quality Matplotlib and Seaborn visuals that tell a clear story from a real dataset.

Module Outcome

What This Module Builds

Students learn to turn raw analysis into clear, compelling visuals that communicate findings to technical and non-technical audiences alike.

Maps to job roles

Data Visualization AnalystData AnalystReporting AnalystInsights Analyst