A mindset for learning

Hello there!

Welcome to this week's edition of my newsletter. Every other week, I share a few thoughts and ideas about data visualization, statistics and web app development.

This week, I've decided to share an essay about a mindset for learning. It is inspired by an idea from Allison Horst.

Even though the essay is framed as a mindset for learning dataviz, I like to think that the idea is applicable to all kinds of fields (including the ones of this newsletter). At the end of the essay, I will also give you a few dataviz resources.

A small warning before we start. Please don't misunderstand this essay as a touchy-feely apology for not wanting to perfect your work. Sometimes there is a need for perfectionism. You can't just submit a half-finished draft to your boss. Or you can't ignore half of the specs that your client gave you.

But for learning a new skill, perfectionism can also be poison. In summary, tread carefully in high-stakes situation but when the stakes are low, embrace your work's incompleteness.

Embrace your shitty plots

Want to hear a ridiculously simple tip to improve your dataviz? Here it is: Allow yourself to create a shitty plot. In fact, embrace the fact that your plot doesn't look perfect. It doesn't have to.

And why should you embrace that? Because it's way too easy to get stuck trying to polish a dataviz. You know, like obsessing over a detail to make your plot "brilliant". If you're trying to learn dataviz, perfectionism will get you nowhere. If anything, perfectionism will totally destroy your motivation to learn more.

So what's the solution? Every time you build a plot, allow it to be crap. But make sure that it is at least a slight degree of newer crap than what you've produced last time.

Small innovations don't perfect your work, but they move you forward

What do I mean by "new"? Let me tell you, my dear reader. I don't want you to create the same shitty plot as you did last time. I want you to branch out and make new crap. Incorporate a new geometric object into your plot. Or try out a new color scheme. Or a new font. Or a whole new chart type. The possibilities are endless. Just pick one and throw that on your pile of crap that is your dataviz.

Why is that a good strategy, you ask? Because it takes some pressure off your shoulders. When you're learning something new, you're already struggling with a thousand things at a time. It's a good idea to take at least the "OMG, is my viz any good!?" factor out of that equation. That's one less thing to worry about.

Making better plots is the goal, collecting "tricks" is the way

But will I ever improve my dataviz skills if I only produce crap? Of course you will! After all, a great dataviz is "only" the result of applying a thousand little moves/tricks/techniques/whatever you wanna call it. And by consistently allowing yourself to produce new crap, you also pick up new tricks of your own. Eventually, you can apply your own thousand tricks. That let's you create a good viz.

But what if I've already learned quite a few tricks? Should I still produce crap? Absolutely! You could argue that is even more crucial now. The more you can do, the more you're tempted to run around in circles. You know, improving a little bit here and there. Without a safety network in place, you can spin in circles. Thus, the same rule applies: It may be not perfect but it's also okay to produce crap. At that stage, chances are that "your crap" is another person's next goal.

Resources

I've recently written a short thread on Twitter that contains a lot of baby steps you can try. But I also want to mention a couple of books that have helped me so much. I've drawn many inspirations from these books. So I think you will like them too.

• Fundamentals of Data Visualization by Claus O. Wilke: A great book that contains everything from different chart types to general design principles. And the best part: it is completely free. You can find it online. A paperback version is available on Amazon if you're a fan of looking at printed data visualizations (I know I am).

• Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic: That was the first dataviz book I bought and it was worth every cent. It's a great starter pack because it is comparatively short. Don't be fooled by its size though. The book contains many, many insights into dataviz and for a short demo, you can check out one of my blog posts. If you want to buy it, you can find it on Amazon.

• Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks by Jonathan Schwabish: Those of you who have only recently joined this newsletter may not know this yet. I am currently reading this book and I am a HUGE fan. This book feels like an encyclopedia of chart types and design principles. There's only little I can't find in this book. That's why I recommend it frequently. You can find it on Amazon as well.

One more little thing before I leave you. Last week, I shared a new blog post. Even though, it doesn't fit perfectly into the theme of today's newsletter, I don't want to let it go to waste. So, check it out if you're interested in learning about alternatives to heat maps. The short version can be found on Twitter.

Alright, that's it. Thank you for reading this week's issue. As always, I am happy to hear your feedback. If you have some, just send me a quick message via Twitter or just reply to this mail.

Enjoy the rest of your day and see you next time!Albert