As an information designer, I’m charged with summarizing data. But even the simplest of questions, like “How big is a typical case?” presents choices about what to do; about what kind of summary to use. An “average” is supposed to describe something like a typical case, or the “central tendency”…
People obsess over what bootcamps to join, the hottest ML algorithms, and which SaaS products to use. But the technicalities of data science/analysis are just one piece of the work.
I would list at least these six skills:
Nice write-up of this position, Erik. I'm of the same persuasion.
I've seen the same thing in the R world, between different dialects. I even wrote a piece comparing one approach (the “tidyverse”) to a kitchen full of gadgets — v. using a chef knife (data.table).
I also saw something similar when I worked in graphic design. There were tool people, who loved add-ons for Photoshop that would save them 10 seconds each, but cost them 2 hours to update and implement in total; versus the people who would just wing it with the tools included, and not worry.
There are some interesting implications for teaching / learning languages too. I think the simplicity approach is far better -- you can learn concepts then, and not baroque tools that you might never need in your particular career.
Are dashboards dead? Kind of. Which I think makes them undead.
No, they're not dead: because people keep asking for them, and other people keep making them.
Yes, they're dead, because they rarely get used: most dashbaords are requested, made, published, and then never accessed again.
Users love the idea of dashboards: that they'll have control, and can get all kinds of insights. But most data are too complex to understand without analysis -- more than you cna do in an interactive dash. So to answer any serious question, you're back to ad-hoc reporting. Which is fine. Dashboards have their uses -- it's just far fewer uses than we sometimes imagine.
Flexibility with constraints. That’s what I find fun and to generate the most creative ideas. So I’ve created a generic system for fantasy classes. (Just remember, half the fun of old D&D games was making your character!)
Each character has three slots to use, assigning each a class archetype from…
If you haven’t done a lot of programming, learning R can be pretty intimidating.
But it’s easier if you focus on fundamentals, and slowly build up your skills through practice. Here I’ll give a short lesson on the most basic things you can do in R.
Last year I started teaching a six-week R programming course at a university, and I have another available online on Udemy, So You Need to Learn R. I thought long and hard about how to teach R. Here’s what I came up with.
I wanted a course that beginners could…
Let me tell you about how to succeed. Obviously, I haven’t yet done this myself, or I’d have better things to do than write about it. Unless you pay me — my speaking rates are on the high end of very affordable, I assure you. But I digress. What was…
You find your data, load it, model it — and get garbage out. Must have been garbage in, as they say. Of course, you made sure to have a nice rectangular files with consistently named columns, and you deleted the random comments people typed into the Excel document. …
There is now a major dialect of R, loudly proclaimed and apparently in the ascendant: the Tidyverse, promulgated by RStudio and largely the effort of one man, Hadley Wickham. Should we adopt it? Should students learn it? Is even having R dialects a good idea?
I have some strong opinions…