Ask HN: Working as a software engineer for 5 years, I've forgotten all CS stuff

Note that I based this analogy on being a CS graduate myself and also working for a while for a chap whose academic speciality was mathematical modelling of things like water hammer.

Discussions regarding PE-style certification for software development often get stuck in the mud, but I'd wager that we'll see something emerge in this generation for general development (as it already exists for safety-intensive applications). And crypto may actually increase the demand for formal proofs and other deep CS concepts.

Your CS prof may have made the remark with some degree of disdain, though there are quite a few programmers / developers out there making better money than a college prof (excluding those working in private consultation).

You gave an example the OS theory would only be good when writing an OS. But the (valid) counter point was that it also becomes relevant when interfacing with an OS.

JavaScript first and foremost runs on your browser, not on the OS itself. So understanding how browsers work (DOM, APIs, XHR...) becomes akin to understanding how the OS works.

(I also have the following rant, which I don't like too much [too polemic] after posting, but I'm keeping it for transparency sake)

No-one claimed you need to know quantum physics to understand what happens to the electrons in your CPU; but generally it's a good idea to know how the next few layers you're interfacing with work (in either direction). And that's often the OS, or the network, or the cache behaviour of your CPU or the superscalar nature of the GPGPU you use to accelerate something important. Or another something attached by some bus to your MCU in the embedded device you're building, or maybe the user navigating that website.

So yea, it depends where you did your CS degree from. Personally, I think it taught me a lot and got us ready for the real world at least as an entry level. Back then, there were no frameworks.

1. CS 101 - I mean, duh? Intro to programming, basic algorithms and data structures. By the end you can at least write a simple program, and if you weren't a nerd like me (started programming at 8 because the computer was there) this would be essential. Which was the case for the majority of my classmates in college, most hadn't done any programming beyond maybe "hello world" before college.

2. CS 102 - Data structures focus, don't recall its name, may have just been "Data Structures". Choosing the right data structure for the job (though many of the particulars aren't necessarily relevant, but how to choose what, that part is critical for embedded systems).

3. Compilers - Parsing, mostly, not the translation part. This was an introductory course, 200-level.

4. CS theory - Main takeaway was choosing between regular and context free languages and understanding the limits of both. Again, parsing but not translation has been more common in my work. The proofs side has been less useful in my professional work, but informal proofs are still very useful.

5. Systems 101 - Baby operating systems, especially working "close to the metal" in embedded, even on a host OS.

6. Computer graphics - Less the graphics part, more the structure of programs to operate at high throughput on large data.

7. Calculus through Linear algebra - (required by the CS degree, 4 courses) highly applicable to some of my work. Especially the recent stuff, touching on orbital mechanics.

8. AI - In the "good old fashioned" sense, not the deep learning/machine learning sense. Turns out it's highly applicable to making expert systems. Probably because that's what it excelled at, and those can be applied to some interesting situations. I'll place this as "indirect" though, like graphics.

9. Computer architecture - Direct and indirect. On the indirect side, it really taught me to work with boolean algebra which turns out to be very useful for studying real programs and their logic (and simplifying them). On the direct side, ever done VHDL/Verilog? Good luck being successful at that without studying computer architecture (self-study or in school)

10. Another computer systems course - Had a big assembly emphasis. Means that I don't "fear" it like many of my colleagues. I can read disassembled code without much trouble, and write it with reference material (because I don't do it often, it's like Fortran to me - I can read it, but writing is slow since I'm unpracticed).

11. Parallel programming - Actually very applicable to some embedded work if you have multiple processors involved or a multi-tasking OS available.

That's 14 courses right there that I've used in my career or am presently using. And that's not counting the math courses I took when I tacked on a math degree. At least I didn't waste 99% of my time. Maybe 10%? But, honestly, I cannot think of a technical course that has not, at some point, come up in my professional work. Even my abstract algebra class from the math degree saved us a lot of time and money a couple times when I proved a requirement was actually impossible.

I can imagine some of my colleagues feeling the way you do, the lack of utility of their degrees. But they all jumped into management roles early on, their technical degrees are useful only for the lingo.

EDIT: I finally thought of a time-waster class. It was our software engineering class. In theory, that should've been one of the more practical classes. Taking requirements, developing solutions, changing requirements, changing the system, etc. In practice, it was all theory. Useless. My AI class was actually a better software engineering class since it was team-project based.

That's your problem right there. Also, the term 'university' seems to be overloaded in so many different ways (multiplied by different countries and cultures).

To me, it seems that the American descriptions and their derivatives are pretty bad, same with the "poly"-prefixed educational institutions. There are so many factors that should be irrelevant (i.e. the "name" of the institute, the cost, the location) where only the subject matter and accreditation should be the actual factors.

If I were to categorise it, you'd have to split it in three directions:

  - Academia, scientific research (University)
  - High-level education, scientific principles and theory, but with application of said principes and theory in mind (College perhaps?)
  - General or vocational education, aimed at learning how to do a job (Trade school?)

The world needs people in all sorts of varieties, but the real-world workload is overwhelmingly one of execution, not one of theory. Of course, when people are stuck in the (rat) race, one must go to university because that is the best, and only the best is good enough. So even if going that far risks people into diving headfirst into their own Peter Principle, it is what people do anyway. The end result are a bunch of people that barely made it through university and aren't all that useful in the real world right away (not advanced enough to propel the entire field or niche forward, not practical enough to perform less theoretical work).

Of course, FAANG only wants the best, and since they can't go screen the 100k+ applications in their inboxes in detail they just start filtering based on paper promises or previous experience, with the end result being a stack of people who either already did the job, or who came out of university with advanced enough results to be malleable into the positions that are available. And thus the path is biased towards "if you want to make a ton of money so you can rent a broom closet in the bay area, you have to work at a FAANG and you can only do that with a university background". Ironically, as large and advanced as those companies may be, most people in the world don't work there. And trading in 80k for half the amount of work and normal living arrangements really isn't a bad deal when working anywhere else that's not a FAANG.

It is especially important to have balance in the undergraduate B.S. degree. At this point, the foundational knowledge is presented, and a broader perspective of the applications of computer science (e.g. software engineering, database design, compilers, operating systems, applied artificial intelligence, etc.) aids in decisions toward which concepts to focus on next whether it be for enterprises or as a researcher.

But I do work for a very large company (not FAANG, but close) and so admittedly, the scale there is far larger than in most jobs, and knowing this stuff really does matter a lot more.