Data Visualization

This is a complete learning path — eight topics that take you from "I don't know which chart to use" to "I can design honest, beautiful, accessible visuals that drive decisions." Everything is taught from zero, in a deliberate order, with practice after every topic and a mini project to cement each skill.

You do not need a design background to start. You only need an eye for detail and the discipline to do the practice. Roughly 10–12 weeks at a few hours a day gets you through the whole path and into a portfolio worth showing employers.

How to use this path

Start with the roadmap, then follow the topics in order — each one builds directly on the previous.

1. Viz Fundamentals — why visualization matters, Gestalt principles, data-ink ratio
2. Chart Types — line, bar, scatter, heatmap, treemap, and when to use each
3. Design Principles — honest design, Tufte, Few, chartjunk removal
4. Color & Accessibility — palettes, color-blind safety, WCAG, grayscale testing
5. Dashboard Design — layout, interactivity, mobile, performance
6. Data Storytelling — narrative arc, annotations, the "so what?" test
7. Tools Deep Dive — Tableau, Power BI, Python (matplotlib, seaborn, plotly)
8. Portfolio & Career — Makeover Monday, TidyTuesday, certifications

Skills you'll build

• Tools: Tableau, Power BI, Python (matplotlib, seaborn, plotly, Altair)
• Concepts: chart selection, design honesty, accessibility, data storytelling
• Work habits: message-first design, grayscale testing, one-chart-per-message

What you'll build

• A chart selection decision framework
• An honest, accessible dashboard
• A 3-page data story deck
• A Python visualization portfolio
• A Tableau Public showcase

Who this is for

• Analysts who want their visuals to actually persuade
• Designers moving into data visualization
• Anyone who presents data and wants to do it better

No experience required. If you can make a basic chart in Excel, you are ready for Topic 1.

Learning philosophy

Four rules keep the path effective. Message over beauty — a chart must answer a question, not just look impressive. Honesty over drama — never truncate, cherry-pick, or use 3D to mislead. Accessibility over aesthetics — if it doesn't work in grayscale or for color-blind viewers, it doesn't work. Less over more — one chart, one message, one "so what?". Follow those rules and the path ends with a portfolio of visuals that people act on.