9 months is 3 quarters. A dedicated student could easily fit 12 courses into that. 12 courses is the bulk of a CS degree (barring a few electives). See for example this one:

https://undergrad.soe.ucsc.edu/sites/default/files/curriculu...

It'd be intensive; but doable.

Forgetting the fact that most schools aren't on quarters, the 12 courses all have prerequisites for a reason. You can't just take them all at the same time.

Even if you could take them all at the same time, maybe 2 or 3% of students would have been capable of completing those courses in that time frame at my University.

You're also cutting out 5 classes because they are called electives. In most programs electives are structured so that you're going to get exposure to certain topics no matter which electives you take, so randomly cutting out 5 classes just because there is some choice doesn't make sense.

I mean "Data structures & Algorithms" is a very broad topic.

The baseline is high-school.

Well "the average CS major" is also a very broad statement :D. I have no good overview of what an average CS major is, to be honest.

I think we are talking about different things. Let's say you learn how to build a hashmap, how to solve TSP with dynamic programming. Conceptually, this is possible to learn in 9 months if you are a good student (and therefore, your are talents are already wasted in this bootcamp). But what if I ask you some follow up questions? Modify the problem. Will you be able to explain how perfect hashing works or what the runtime of a hashmap is if the hashing function is not O(1). How different hashing procedures lead to different qualities for different implementations of hash maps?

We are not talking about knowing that hashmap give you O(1) lookup if you are lucky. If that is the skill you want to learn, sure, 9 months will do. But truly understanding what you are talking about and being able to explain, augment, modify/improve data structure and suit algorithms to your needs... Proving that they still work correctly after your modification. Understanding how that damn distributed consensus algorithm works that seems to have a bug that fucks up your database every now and then.

Yes agreed. You don't need to know that for 99% of CS jobs. But we are not talking about whether bootcamps can prepare you for work in average code mills, we are talking about whether they can replace CS education.

As a matter of fact I DO believe that bootcamps solve a critical purpose in filling the vast amount of gaps in our IT market. But I am always surprised again and again why "the new kid on the block" always needs to attack other completely valid paths (like CS major). They are completely different things made for a different purpose.

I think people need to realize that CS major is NOT the right choice for most coding jobs. But that does not mean that a bootcamp can replace a CS major, it just means that a bootcamp can be an efficient shortcut to hit the job market running.

> Yes agreed. You don't need to know that for 99% of CS jobs. But we are not talking about whether bootcamps can prepare you for work in average code mills, we are talking about whether they can replace CS education.

We're discussing "what happens to tech workers when their skills become obsolete." If being able to contribute to a scaled system as a great software engineer at Google or Amazon is not a sufficient measure of "Knowing CS well enough," I'm not sure we're talking about the same thing.

Is it enough CS to do fundamental AI research? Eh probably not. Is it enough CS to do pretty much any other job out there? Yes.

It probably doesn't even matter for AI research. I don't know any professional AI researchers, but I bet reading arxiv and reproducing other's work, and publishing your own work is how you go about that. Why would you do coursework when there's papers to read?

_Maybe_ you don't need that much CS per se (debateable, but an argument can be made); however you do need a lot of math coursework.

Also, it's far easier to grasp the basics of AI from a course than to reconstruct "basic body of knowledge" just from reading arxiv. As a matter of practical considerations, I just don't believe anybody can become an expert by reading only the research, without going first through the basic training. It's too damned difficult, too much work (and pointless, too - people did that work for you already and built great courses with the summaries, why not take advantage of that?

>how to solve TSP with dynamic programming.

Impressive. (TSP is NP-hard. Not even NP-complete because you can't verify the solution in polynomial time... or at least, I don't know how to do it and would love to see a solution with dynamic programming)

It's an approximate solution with bounded unoptimality, I don't remember the exact bound off the top of my head.

Edit: it's 3/2 for Christofides' algorithm. Native dynamic is bad speed but gives accurate solution. It's no good for more than 24 nodes or so.

Usually it is good enough, other similar and better attempts try to tighten that bound with more admissible heuristics. E.g. for metric spaces solution is possible to tighten a lot. (Like shortest travel without weights.)

Oh, I see what you mean; I would still be seriously impressed with any bootcamp that teaches approximation algorithms. And not in a good way TBH - it kinda' defeats the purpose of the bootcamp. It's hard enough to go through Knuth in a few months...