Effective Data Visualization with Altair
Make charts that say something true, clearly, to the reader you have in mind, and finish with one from your own data that you can defend.
You go at your own pace, with your Maestro beside you, 1:1, through the whole course: ask it anything, and it asks you questions too.
29 skills · 12 modules
$30
Presale · The first module is on its way.
I'm opening the course one module at a time. When every module is out, the course will be $80. What you pay today covers all of them.
Comes with your Maestro, 1:1, for the whole course.
What will you be able to do?
Each box is a skill. The base is at the bottom, recognizing and explaining, and you work your way up to building something of your own and judging what works and why. Hover over a box to see what it builds on. Click it to see where I teach it and how your Maestro checks it.
- Evaluate
- Synthesize
- Analyze
- Apply
- Understand
- Remember
Evaluate
Defend visualization design decisions with reference to principles and audience
Builds on:
- Critique a visualization using multiple frameworks simultaneously
- Analyze audience needs and reading context for a visualization
Your Maestro asks you to judge when it fits and why and saves it as evidence when you do.
Synthesize
Revise and improve a visualization in response to critique
Builds on:
- Critique a visualization using multiple frameworks simultaneously
- Customize chart aesthetics in Altair: themes, colors, titles, axis labels
Your Maestro asks you to build something of your own with it and saves it as evidence when you do.
Articulate effective visualization norms and expectations within one's own field
Builds on:
- Encode provenance, uncertainty, and human context into a visualization
- Critique a visualization using multiple frameworks simultaneously
Your Maestro asks you to build something of your own with it and saves it as evidence when you do.
Produce a portfolio-ready visualization with written defense of design decisions
Builds on:
- Revise and improve a visualization in response to critique
- Defend visualization design decisions with reference to principles and audience
Your Maestro asks you to build something of your own with it and saves it as evidence when you do.
Analyze
Analyze audience needs and reading context for a visualization
Builds on:
- Explain why visual encoding aids cognition over text or tables
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Identify chartjunk and non-data ink in real-world visualizations
Builds on:
- Explain Tufte's data-ink ratio and its implications for design
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Critique a visualization using multiple frameworks simultaneously
Builds on:
- Apply Tufte's principles to simplify and clarify a chart
- Match chart type and encoding to communicative intent and data structure
- Apply humanizing strategies — context, individuality, emotion — to a dataset
- Customize chart aesthetics in Altair: themes, colors, titles, axis labels
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Apply
Create a basic chart in Altair from a tidy dataset
Builds on:
- Explain the grammar of graphics: marks, encodings, scales, and coordinate systems
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Use Altair encoding channels: x, y, color, size, shape, tooltip, facet
Builds on:
- Create a basic chart in Altair from a tidy dataset
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Select appropriate mark types in Altair for the data and intent
Builds on:
- Create a basic chart in Altair from a tidy dataset
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Apply Tufte's principles to simplify and clarify a chart
Builds on:
- Identify chartjunk and non-data ink in real-world visualizations
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Match chart type and encoding to communicative intent and data structure
Builds on:
- Explain Cairo's framing of chart types as a visual language with dialects
- Explain how human perceptual limits affect chart comprehension
- Analyze audience needs and reading context for a visualization
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Apply humanizing strategies — context, individuality, emotion — to a dataset
Builds on:
- Articulate Lupi's Data Humanism philosophy and its critique of pure efficiency
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Understand
Explain why visual encoding aids cognition over text or tables
Builds on:
- Define data visualization and its communicative purpose
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Explain the grammar of graphics: marks, encodings, scales, and coordinate systems
Builds on:
- Define data visualization and its communicative purpose
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Survey Tufte, Cairo, and Lupi as complementary frameworks
Builds on:
- Define data visualization and its communicative purpose
- Explain why visual encoding aids cognition over text or tables
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Explain Tufte's data-ink ratio and its implications for design
Builds on:
- Survey Tufte, Cairo, and Lupi as complementary frameworks
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Identify and explain visual variables: position, color, size, shape, texture
Builds on:
- Survey Tufte, Cairo, and Lupi as complementary frameworks
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Explain how human perceptual limits affect chart comprehension
Builds on:
- Identify and explain visual variables: position, color, size, shape, texture
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Remember
Define data visualization and its communicative purpose
Your Maestro asks you to recognize and recall it and saves it as evidence when you do.
Who is this course for?
For people who want to make charts that say something true, clearly, to the reader they have in mind. You write Altair code, practice on Gapminder's life expectancy data, read charts through Tufte, Cairo and Lupi, and finish with a chart from your own data that you can defend. Modules open as they are released.
Ask your Maestro
Your Maestro is an AI that knows each course, page by page. Try it here free, in the free course "Which chart should I use? Choose and read charts that tell the truth".
You: Where do I start?
Your Maestro: From what you already know. Tell me what you've done with data, and I'll start this page from there.
Asking about: Answers from the page "Did it grow more in pesos or in percent? A chart's scale"
Only this browser keeps what you ask here. How it works
What we'll cover
1. What is Effective Data Visualization?
4 pages to come
Opens as soon as it's ready.
- Welcome
- The four questions
- A gallery of the unexpected
- Your working definition
2. The Anatomy of a Chart and Hello Altair
4 pages to come
Opens as soon as it's ready.
- Meet the dataset
- The anatomy of a chart
- Altair as sentences
- Write your first chart
3. Grammar of Graphics
3 pages to come
Opens as soon as it's ready.
- The grammar of graphics
- First exploration: life expectancy
- Data types in practice
4. Who Are You Making This For?
3 pages to come
Opens as soon as it's ready.
- The same chart, different audiences
- Meet the three lenses
- The portfolio seed
5. The Efficiency Lens — Tufte
3 pages to come
Opens as soon as it's ready.
- The data-ink ratio
- Identifying chartjunk
- Apply the lens to your own chart
6. The Language Lens — Cairo
4 pages to come
Opens as soon as it's ready.
- Visual variables
- How humans perceive charts
- Chart types as vocabulary
- Choose and defend
7. The Human Lens — Lupi
4 pages to come
Opens as soon as it's ready.
- What is Data Humanism?
- What a correct chart leaves out
- Add humanity to your chart
- Carry forward: your field and the three lenses
8. Shaping Your Data in Altair
4 pages to come
Opens as soon as it's ready.
- Transforms as questions
- Filter, aggregate, calculate
- A table is a display too
- Layering
9. Color, Annotation, and Finishing
3 pages to come
Opens as soon as it's ready.
- Color with purpose
- Annotation and titles
- Finishing in Altair
10. Critique and Iteration
4 pages to come
Opens as soon as it's ready.
- How to critique
- Critique a published chart
- Critique your own chart
- Iterate
11. Effective Visualization in Your Field
3 pages to come
Opens as soon as it's ready.
- What good looks like in your field
- Finding your dataset
- A first sketch with your data
12. Produce and Defend
4 pages to come
Opens as soon as it's ready.
- Production
- The written defense
- The Socratic defense
- The return to Lesson 1
How long does it take?
3 to 5 hours
A guide: you go at your own pace, and the course waits where you left off.
Common questions
- Is this course finished?
- Not yet. It is in presale, and its modules open as they are released. What you pay today covers every module as it comes out. It has twelve modules at your own pace, from your own definition of effective visualization to a portfolio piece built from your data. The course is in English.
- What will I have at the end?
- You will have a chart from your own data that you can defend, plus a way to read charts through three lenses: Tufte on efficiency, Cairo on visual language and Lupi on Data Humanism. You also learn to shape data with transforms, finish with color and annotation, and critique and revise your work.
- Do I need to know Python to learn Altair?
- Basic Python, yes. Through Lesson 10 every chart runs right on the page, with Altair imported and the Gapminder data already loaded, so there is nothing to install. Altair code reads like a sentence: take this data, make points, encode x and y. For Lessons 11 and 12 you use a notebook with your own data.
- Do I need a course, or can I learn this on my own?
- You can learn it on your own. The course gives you an ordered path from your first chart to a portfolio chart from your data, critique along the way, and your Maestro answering your questions. Unsure? Start with a free course such as Which chart should I use? If it's not for you, you have 7 days for a refund.
How do I check what AI does? I teach it in Your AI and Your Data: Don't Let It Make Up the Number and Check what AI gives you in three steps.
What you get
- 12 modules with 43 pages. I'll open the next modules as each one is ready.
Your Maestro for the whole course, who always knows which page you're on: you open it from there.
It works from this course's skills, the ones above.
Ask it how you're doing, and it tells you what you've already shown and what comes next.
Who's teaching you
Sergio Sánchez
Data visualization is what I love most. It's how I get an idea from my head into yours as clearly as I can, so that's where I started. With a tutor on every page, this is the first time I feel we can teach well at scale, and I'm excited to share this course with you.
I'm Sergio, chekos online. I'm a data architect, and I've spent more than ten years working with data in the Bay Area, mostly at mission-driven organizations: policy research first, then education, and now early childhood services. I grew up in Tijuana. In 2018 I started tacosdedatos to share what I was learning, and since then it's been a blog, a podcast, courses, guides, and a community. These days I spend a lot of my time building with AI agents and writing about how I organize my work around them. I share what I've learned; you take what's useful and make it yours.
The course in brief
| Price | $30 USD, paid once |
|---|---|
| Pages | 43 in 12 modules |
| Time | 3 to 5 hours |
| Language | English, Spanish |
| Format | Online, at your own pace |
| Maestro | Your own 1:1 AI tutor for the whole course |
| Refund | In full, within 7 days |
See you in the course
Effective Data Visualization with Altair