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Comment by 90s_dev | Hacker News Reader

To be fair, if you learn computer science well enough to thoroughly understand Scheme, I don't think it'll take more than a few weeks during the summer to learn Python.

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I disagree. You can learn the language itself pretty quickly. Finding your way through the expansive standard library will take longer. Getting a good handle on the package ecosystem is a lifetime learning project.

Knowing your way around the ecosystem of one programming language does not build up the intuition necessary for identifying O(n²) (or worse!) algorithms and choosing/writing O(log(n)) (or better!) ones instead.

Computer Science has little to do with science, but what it teaches you is certainly closer to science than just building a huge mental index for a bunch of work done by other people.

There's certainly value in that skill, but it has no place in a Computer Science curriculum.

This would be like taking Astrophysics students and telling them to study the details of all of the different kinds of telescopes they can buy.

That's not really what we were talking about here. The context was that compsci students want to learn marketable skills, and the claim was that if the student had learned Scheme in class, they could quickly pick up Python. And that's true, for the language itself. But knowing only the language, like never importing code you hadn't written yourself, doesn't go far along the road to marketability.

I'm not arguing that compsci should be job training, not at all. My disagreement is solely with this specific claim.

But FWIW, while I understand your analogy, an astrophysics department that didn't tell the students that there are these things called telescopes, and here's why you might use one over the other for various situations, and that they're how you're going to get the observation data you'll test your theories against, would be doing a disservice.

You can learn about algorithmic complexity in python. Why not just focus on one language to keep things more focused.

> Knowing your way around the ecosystem of one programming language does not build up the intuition necessary for identifying O(n²) (or worse!) algorithms and choosing/writing O(log(n)) (or better!) ones instead.

I'll disagree with this, at least in terms of Scheme versus Python.

Python is visually close enough to other languages that the skills you develop to quickly see O(n²) algorithms easily transfer to many other languages. Scheme is very different visually, and so the intuition doesn't transfer as well. Sure, it's possible your intuition is wrong, but when scanning a program intuition can help in the first pass.

Scheme has (trace), at least most interpreters (and his cousing Common Lisp) have some tracing and pretty-printing features. Far more powerful than anything Python could offer.

Oh, and of course it has functions like sdraw or draw-cons-tree when you can print the contents of a list in seconds as an ASCII-ART chart:

https://www.t3x.org/s9fes/draw-tree.scm.html

The file it's in the public domain.

Try that with Python.

Python has the pprint module, which takes care of this for you, and for more datatypes than are done here. (I don't see how this would handle a hashmap in a sensible way.)

But I'm not sure how this addresses what I was saying, which is that the intuitions about algorithms you get working on Python are easier to transfer to popular languages like C++, Java, Javascript, Rust, etc..

This depth you are never going to get in a college education anyway. Especially not since programming isn't (and shouldn't be) the only thing you learn in a Software Engineering / Computer Science bachelor.

Those are things though, that you can easily make up for, for the most part, with good web search skills, or these days, AI prompting skills.

One of the big shifts in academia over the past couple decades is that, for any number of reasons, students today are less likely to self-study or tinker outside of classes and internships. The increased prevalence of basic bootcamp-style classes like "Let's Build a Rails App" in CS programs is because departments can no longer assume that students will explore things like that in their spare time.

What good does that do, though? Make it harder to tell the intrinsically motivated students from the “I’m just here to get a job when I graduate”? It seems like it harms the former.

Is that what we need from universities? Is that helping employers? Helping strong or intermediate students?

It's what universities have become. They are expensive, grandiose trade schools operating out of very distinguished-looking Collegiate Gothic designed buildings.

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