I can’t see it as anything but a matter of phonetic spelling, hence the reluctance of Europeans and Canadians to use #dataviz. Just like business is shortened as biz, Americans shorten visualization as viz.

To me … as silly a debate as data is v. data are.

All students were awe inspiring.

In the end, “Donicelas” —professional jury— and “Raposos” —popular vote— won.

(Yah, we took our team names from the Galician forest 🌳 🦊 🐗 )

Next week, we’ll merge all the PRs and publish the results.

Take a great tools like Flourish or Datawrapper. Nothing prevents you from making terrible, absurd interactives or data visualizations with it. If your data reporting is bad or you misuse visual encodings, it will show. Tools by themselves won’t save you. Learn why, then how.

But that doesn’t mean tools are a panacea. They ain’t what will rescue digital journalism. Just like a great camera don’t make a photographer great, digital tools for journos don’t make visual journalism great. Tools give access; understanding gives mastery. They don’t replace critical thinking.

Yes, tools help remove barriers and make fields more accessible. Yes, developing tools is fundamental to a field’s progress. And yes, I can even indulge and assume McLuhan’s point of view —I’m an integrati: I can’t help but see the positive in mass access to otherwise esoteric fields of knowledge.

Replies encouraged.

I'd love to know what other instructors do or other practitioners would expect to learn:
- Is the syllabus too broad or too narrow?
- Traditional, flipped, or a mix of both teaching methods?
- What other activities do you do to engage folks in thinking critically about dataviz?

The part we do in @observablehq.com is public here: observablehq.com/collection/@... We just started, so our second session is next week.

I still need to find a way to open the rest of the materials, especially the conversations. They're currently on the University's Moodle.

It's 6 classes and a hackathon —shared with the Project Management subject. The 6th session is reserved for the final project presentations.

The syllabus is:
- Why Visualize
- A Visual Language
- Let's Talk Color
- The Age of (Data) Exploration
- Data-driven visual storytelling

To keep a record of these convos, every week, we have an annotation team in charge of taking notes of the discussions in the '𝗟𝗲𝘁'𝘀 𝗱𝗿𝗮𝘄', '𝗦𝗽𝗲𝗮𝗸𝗶𝗻𝗴 𝗼𝗳 𝗰𝗵𝗮𝗿𝘁𝘀', the '𝗥𝗲𝘃𝗶𝗲𝘄 𝗼𝗳 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸'𝘀 𝗲𝘅𝗲𝗿𝗰𝗶𝘀𝗲', and '𝗙𝗶𝗻𝗱 𝗮 𝗚𝗼𝗼𝗱 𝗧𝗵𝗶𝗻𝗴' segments —and the notes are graded as part of their participation grade.

𝗙𝗶𝗻𝗱 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴 is a way to stay positive when faced with horrific charts. Every week, a few students bring bad charts they've seen out there, and we all have to find something good to say about it, any aspect where things didn't go completely wrong.

We discuss:
- Battling Infectious Diseases in the 20th Century: The Impact of Vaccines graphics.wsj.com/infectious-d...
- How the Recession Reshaped the Economy, in 255 Charts www.nytimes.com/interactive/...
- Codex Atlanticus codex-atlanticus.ambrosiana.it/

Among others.

𝗦𝗽𝗲𝗮𝗸𝗶𝗻𝗴 𝗼𝗳 𝗰𝗵𝗮𝗿𝘁𝘀 are conversations about a data visualization example tied to the class topic. We'll attempt to reverse engineer how their authors went from data query to visualization, the strengths and opportunities of the charts, and what we can learn from them.

The class is 3 and ½ hours long, so imagine if it were just me talking 😱 so I keep that in check.

My favorite bits are: '𝗟𝗲𝘁'𝘀 𝗱𝗿𝗮𝘄,' '𝗦𝗽𝗲𝗮𝗸𝗶𝗻𝗴 𝗼𝗳 𝗰𝗵𝗮𝗿𝘁𝘀,' and '𝗙𝗶𝗻𝗱 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴.'

𝗟𝗲𝘁'𝘀 𝗱𝗿𝗮𝘄 are warm-up, pen-and-paper exercises that set the topic for the class. I also use these in workshops.