The standard entomologist curriculum does not require calculus, while a physics curriculum does. Both produce scientists. (For example, https://cals.cornell.edu/education/degrees-programs/entomolo... under "Major Requirements" says "One semester of college statistics or biometry", and the listed physics requirement doesn't require calculus.)
On the other hand, an entomologist interested in population ecology may need to know differential equations.
Your use of "study program" suggests your experience is at the undergrad level, and not at the grad school level, which is how most scientists I know got their training.
At the undergrad level the study programs do reflect what's needed for a solid education. If a student is interested in computational biology, that program will emphasize taking more CS courses than the program for a student interested in marine biology.
But at the grad level, the "study program" is much less formalized. You might take graduate level classes the first couple of years, but then you are expected to pick up the missing bits on your own.
Once you have your PhD and are a working scientist, you rarely have the luxury of following any study program.
And if you've been a scientist for 20 years, any CS training you had likely did not cover SIMD, and emphasized practices which are no longer relevant. (For example, the link points out "That advice [about HDDs] is mostly outdated today [with SSDs]".)
Those latter categories are who the linked-to piece is for, not undergrads in a well-defined study program.
I would be curious to know of all the "scientific coders" what percentage of them understood the entire article. I'd be similarly curious how much your typical "bootcamp" developers would understand of it. I know everything presented, so it basically comes off as a "lecture notes" for someone that already knows it. Someone that doesn't understand SIMD, CPU fundamentals, assembly, and compilers, I'd imagine their eyes would glaze right when the assembly code appeared.
And while SSDs are MUCH FASTER than HDDs, the basics of interacting with storage is the same, just that rather than waiting a million years for data to arrive from the CPU's perspective, it comes in 10,000s of years.
Latency numbers all programmers should be aware of:
I can't judge background - I don't have a sense of who uses Julia, and I've been programming for too long, without exposure to the target audience.
Since you mentioned "academic setting", I'll point out there are also scientists-who-program in industrial settings. However, none of the ones I know about use Julia.
My belief is that most scientists-who-program aren't going to read text books from other fields. They are under pressure to produce NOW, and don't think it's worth the time to acquire an entirely new mindset. Instead, I think this sort of knowledge transfer is by jerks and fits, as someone figures out an optimization, and passes it along, with domain-specific context that makes it easier for others in the field to understand.
Which means, like you, I don't think this notebook will be all that useful, though in my case that's because I think it's too generic.
> what percentage of them understood the entire article
I don't think that's a telling metric. Only some scientific coders are interested in writing fast code (vs. fast-enough code), and only some of those use Julia.
Can you elaborate a bit? I don't really get what you are trying to say.
