Fundamentals First, Tools Second
Learning tools makes us faster. Learning fundamentals makes us better.
We all love shortcuts. Laziness is part of being human. And that’s not necessarily bad. Many great inventions started with someone trying to save time and/or effort. But there’s a line between useful shortcuts and laziness that prevents us from doing things well. Or even from doing the right things.
In the past five years, I’ve seen more LinkedIn posts than I can count with titles like “Python vs. R”, “Tableau vs. Power BI”, “This vs. That.” But here’s the real question: Does the tool even matter if the end result looks terrible because you learned only the tool and not what to do with it?
Take cooking. A sharp knife speeds things up, and a fancy blender makes smoother purées. But without knowing seasoning, heat control, and balance, the food falls flat. The same principle applies to data visualization.
I don’t want to call anyone out. Learning in public is tough, and I admire anyone with the courage to do it. But you’ve seen the dashboards: slicers, charts, and titles in the right spots, but no cohesion otherwise. The chart types don’t fit the data. And they use everything. If the dataset has 20 columns, you’ll see 20 charts. Fine for exploratory data analysis (EDA)1, less fine if you’re telling a story.
That’s the data equivalent of dumping every spice in your pantry into one pot and hoping for the best. We’ve all seen how that turns out. No thanks.
Learning in public should be encouraged. But encouraging bad practices without feedback isn’t helpful. It’s like those talent shows where contestants can’t sing, but no one ever told them before. In the workplace, the talent show moment is the job interview. Too late for honest feedback.
The Shortcut Trap
Part of the problem is that, in 2025, data literacy still lags where it should be. And everyone has an opinion on data visualization. Most of that advice? Noise. Add GenAI to the mix, and now we also have models trained on bad examples, churning out even more of them.
The truth is, this isn’t new. Long before AI, we had cheat sheets and chart pickers. Nothing wrong with them, after you’ve learned the fundamentals. But if they’re all you have, you’ll never know why a chart works, just how to click it into existence.
And why are tools pushed the way they are? Because they sell. Knowledge? Not so much. Marketing promises that This Amazing Tool™ will solve everything, and without code! Reminds me of an old Finnish comedy sketch from Kummeli. They advertised a lure called Catch a Fish. Not only did it catch the fish, it gutted and cooked it, chopped your wood, and even did your taxes! Sound familiar?
Tools Are Not the Enemy
Don’t get me wrong. I like tools, a lot. I even have a 1988 book called Learn to Draw Charts and Diagrams Step by Step by Bruce Robertson. Trust me, we don’t want to go back to drawing charts by hand. Tools make us faster. But speed without judgment is dangerous. The question is:
Are tools helping you do your job better? Or just making it easier to create nonsense?
As the saying goes:
“Just because you can doesn’t mean you should.”
Why Fundamentals Get Sidelined
Another reason fundamentals are undervalued: career paths. I recently saw a diagram ranking data careers. Level 4: Data Analyst. Level 5: Data Engineer. The implication is that engineering is above analytics. That’s nonsense. These are two distinct, equally necessary disciplines. One produces the pipeline; the other makes meaning out of it.
Yet pay tells a story: data engineers generally earn more than analysts with similar skills. As long as analytics is undervalued, visualization quality will lag.
“If you pay with peanuts, you get monkeys.”2
Why Fundamentals Are Hard
David Epstein put it best in Range:
“It is difficult to accept that the best learning road is slow, and that doing poorly now is essential for better performance later.”
- David Epstein: Range
Mastery takes time. No ad promising data viz mastery in 7 days can change that. But the paradox is this: you don’t need mastery to do practical work and to be useful. Fundamentals deepen as you apply them on the job. Nothing beats solving a real problem.
The trap is complacency. Thinking you’re good enough or that you don’t have time to improve. Churning out charts fast feels productive. But without fundamentals, you’re just repeating mistakes faster.
Why Fundamentals Still Win
There’s an old line from The Bride of Lucky Luke:
“In the land of the blind, the one-eyed man is the king.”3
If everyone else is chasing tools, being excellent at fundamentals makes you stand out from the crowd. Salaries may vary, but being outstanding usually puts you at the higher end of the range. More importantly, your work will be more satisfying when you know it works.
And here’s the thing: tools come and go (SQL might be the exception). Fundamentals stick. They transfer. They even make it easier to learn new tools. With LLMs, learning tools have never been faster. But only if you know the fundamentals well enough to ask the right questions.
Where to Start
It’s daunting, yes. But fundamentals aren’t a one-and-done. They’re a cycle. You return to them, and each time, you learn something new.
As you can see, I didn’t just give you a list up front. A list without context is useless. Or, as Beverly Sills said:
“There are no shortcuts to any place worth going.”
But now that you have the context, here are some starting points:
Books
Alberto Cairo - The Truthful Art
Alli Torban - Chart Spark
Andy Kirk - Data Visualisation
Claus O. Wilke - Fundamentals of Data Visualization
Cole Nussbaumer Knaflic - Storytelling with Data
Edward R. Tufte - The Visual Display of Quantitative Information
Eva Murray & Andy Kriebel - #MakeoverMonday
Jonathan Schwabish - Better Data Visualizations
Juuso Koponen & Jonatan Hildén - Data Visualization Handbook
Nick Desbarats - Practical Charts & More Practical Charts
Stephen Few - Now You See It
Courses
DataCamp - Understanding Data Visualization
Yan Holtz & Cédric Scherer - ggplot2 uncharted
While this one has a tool element (R/ggplot2), the course also takes the time to cover the fundamentals that are then implemented using ggplot24.
People to follow
In conclusion
Learn the tools, yes. But master the fundamentals. Tools make you fast. Fundamentals make you good.
“In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.” Source: Wikipedia
Or as we say in Finland: “If you pay with bananas, you get monkeys.”
Etymology: “Calque of Latin in regione caecorum rex est luscus, popularized by Desiderius Erasmus’ Adagia (1500). For further origin, compare Aramaicבשוק סמייא צווחין לעווירא סגי נהור (literally “in the street of the blind, the one-eyed man is called the guiding light”), found in the Genesis Rabbah (4th or 5th century CE). Source: Wiktionary
ps. The quote says man and king. I left it there to be truthful to the original, but this absolutely applies to you, no matter how you identify yourself.
Of course, I’m totally biased here. I’m writing a book called ggplot2 extended. How could I not be?