Welcome

Welcome to Introduction to Data Visualization with Python and Plotly Express. This course is written for someone who has never made a chart in code before — perhaps you have never written a line of Python, perhaps you have only ever seen graphs in a newspaper or a school textbook. You will not need a statistics background, you will not need to install anything, and you will not need to memorize syntax.

What you will do is learn to see data. By the end of this course, when you look at a table of numbers you will instinctively reach for a chart, and you will know which chart to reach for.

What this course is — and is not

This is a course about visual reasoning. We will spend more time talking about why a chart works than typing chart code. We will use Plotly Express — a small, friendly Python library that lets you go from a table to an interactive chart in a single line — but Plotly Express is the vehicle, not the destination.

This course is not:

• An advanced statistics or machine-learning course.
• A frontend engineering or React/D3.js course.
• A deep dive into enterprise BI platforms like Tableau or Power BI.
• A book on mathematical proofs of perception.
• A guide to deploying production dashboards on Kubernetes.

This course is:

• A guided tour of the ideas behind good visualization.
• A practical workshop on Plotly Express, one chart at a time.
• A primer on data storytelling and visualization ethics.

What you will learn

The course is organized as a journey:

1. The story — How humans went from carving tally marks on bone to building interactive dashboards in a web browser.
2. Visual reasoning — Why charts work at all, and why some charts work better than others.
3. Plotly Express fundamentals — The smallest amount of Python you need to make your first chart.
4. Core chart types — Bar, line, scatter, histogram, box, pie, heatmap, bubble. One per page, with the when and why in front of the how.
5. Encodings, color, and layout — How charts gain their meaning from columns mapped to position, color, size, and shape.
6. Workflows and domains — Time series, geographic maps, filtering, and the rhythm of an exploratory analysis.
7. Storytelling, ethics, and practice — How to turn charts into arguments, how to avoid lying with graphics, and how to debug the visualization that "just looks wrong."

How the interactive pages work

Every Python code block on these pages runs in your browser via Pyodide — a full CPython 3.13 build compiled to WebAssembly. There is nothing to install. Plotly, Pandas, NumPy, and the sample datasets all ship with the runtime.

You will see three kinds of widgets:

1. Executable code blocks. Edit any snippet and click Run. Charts appear inline and are fully interactive — hover, zoom, pan, and toggle legend items.
2. Multiple-choice questions. Quick conceptual checks. Some pages have several, especially the foundational ones — getting the intuition right early on saves you hours later.
3. Callouts that flag tips, warnings, and "why does this matter?" moments.

A taste of what is coming

Here is a complete interactive visualization you will be able to write — and understand deeply — by the end of this course.

If that looks intimidating right now — good. Every line of that chart will feel obvious by the time you reach the end of the course.