[feoren]

> ridiculous amount of math and math proofs ... [vs.] a course designed to teach programming and programming concepts

Math, big-O, and proofs are programming concepts. If you want a shallow understanding of whatever programming languages and frameworks are in vogue this year so you can be handed down constrained requirements in a code mill where you are evaluated on how many lines of code you write per day, take a coding bootcamp. But know that after a few years your skills will be out of date and you will have a hard time keeping up with the field. If you want to solve hard problems that haven't been solved before, pay attention to that math and those proofs. I'm honestly just sick of people complaining about CS degrees for not spending enough time teaching React JS or Ruby on Rails. CS degrees are for people who want to solve problems that are actually hard and new.

Seriously, do we ever hear physics majors complaining that they have to learn all this math that they're never going to use, in order to study the foundational cosmology of the universe? Why don't they just show me how to work the damn telescope!?

[derefr]

> CS degrees are for people who want to solve problems that are actually hard and new.

No; not any more than theoretical-physics degrees are for people who want to solve "hard and new" automotive-engineering or pharmacology problems. That there exist lessons from a given academic discipline that have practical application within a given profession, does not mean that you need to become an academic of that discipline (i.e. someone who can advance the state of the art in that discipline — which is what "getting a degree in X" means, if you're doing it right) in order to become a professional in said profession; or even to advance the state of the art of the profession (rather than of the associated academic discipline.)

In schools for professional (rather than academic) disciplines — e.g. medical schools, law schools, trade schools, etc. — the lessons from academia with relevant practical application to your field are taught together with the more practical material. For example, in learning to be an optometrist, you learn optics. That's physics! But it's only a certain part of physics, and it's presented through the lens (heh) of the problem domain that you care about.

Coding boot camps are shit, I'll agree. Software Engineering programs aren't. I'll take a professional Software Engineer over an academic Computer Scientist any day — especially to have on my team when working on entirely-novel problems. The professional has been taught the problem space, whereas the academic only knows the solution space. It's a lot easier to have a professional read a few books and papers to learn about the solution-space relevant to solving their problem; than it is to fix an academic's lack of appreciation of the constraints imposed by the problem being solved.

[barry-cotter]

> In schools for professional (rather than academic) disciplines — e.g. medical schools, law schools, trade schools, etc. — the lessons from academia with relevant practical application to your field are taught together with the more practical material.

Teachers mostly don’t think their education degree made them better teachers and there’s no evidence they do[1]. It’s widely agreed that at least the third year of USAn law school is useless[2] and there are testing providers whose entire thing is teaching graduates what their law school didn’t but should have if it was professional training [3].

Professional schools are run for the benefit of the staff, so they teach what the admins and teachers want to teach. It has to have some relationship to the field but it can be completely attenuated. People learn to do their job at work, not on a university campus.

[1] It's easier to pick a good teacher than to train one: Familiar and new results on the correlates of teacher effectiveness

https://www.science-direct.com/science/article/abs/pii/S0272...

[2] https://www.businessinsider.com/third-year-of-law-school-is-...

https://forgottenattorney.wordpress.com/2013/02/01/the-usele...

[3] https://abovethelaw.com/2017/05/teaching-you-what-law-school...

[derefr]

Education is IMHO uniquely problematic / a bad example of a professional discipline, because nobody knows what works. Academia is horrible at running RCTs on education methods; and industry is horrible at incentivizing good teaching (i.e. ineffective teachers, whether in kindergarten or college, never get fired just for being ineffective.) Our current "theory of education" is probably just 1000 P-hacked studies in a trenchcoat.

It's likely pretty easy to measure that lawyers from professional law schools win more cases than self-taught lawyers. Or that doctors from medical schools have higher patient satisfaction / produce higher average QALYs in their patient cohort than self-taught doctors.

I think a more closely analogous question to the one of CS vs SWEng might be: if a group of psychiatrists (professionals) and psychologists (academics) switch places, who performs better in the other context?

[seanmcdirmid]

> if a group of psychiatrists (professionals) and psychologists (academics) switch places, who performs better in the other context?

The psychiatrist is an MD who can prescribe drugs. The psychologist wouldn’t have that kind of authority, so they can’t do the job of the former. A psychologist can actually do therapy, which a psychiatrist isn’t trained for. These are very different professions.

[barry-cotter]

Psychiatrists in the US overwhelmingly have to be trained in psychodynamic therapy, aka Freudianism. The only thing we know for certain works in therapy and that has worked consistently is the client and counselor having a good relationship.

> Conclusions and Recommendations of the Interdivisional (APA Divisions 12 & 29) Task Force on Evidence-Based Therapy Relationships

http://societyforpsychotherapy.org/evidence-based-therapy-re...

> The therapy relationship makes substantial and consistent contributions to psychotherapy outcome independent of the specific type of treatment.

> The therapy relationship accounts for why clients improve (or fail to improve) at least as much as the particular treatment method.

[sdenton4]

omg, my least favorite thing in the education literature:

Prof does a 'study' where they teach a class with both hands vs with one hand tied behind his back. Each class has 200 students, and he finds that the one-handed class outperforms the two handed class with p=0.045 with N=200...

And I'm like NO! This is basically an N=1 study because the teacher is common across both classes. Have you never heard of pen-effects?!?!?!

Unfortunately, fixing the problem in the study design means convincing a bunch of your friends that one-handed teaching might be better and engaging in an experimental study together... But that's obviously way too hard.

[_zywo]

Slightly OT, but if you want an official word for "USAn", you can try using "usonian".

[Aeolos]

> I'll take a professional Software Engineer over an academic Computer Scientist any day — especially to have on my team when working on entirely-novel problems.

Fully, 100%, whole-heartedly agreed!

I'm leading a multi-disciplinary machine learning R&D team comprising multiple experienced Computer Scientists, Software Engineers and one Electrical Engineer who jumped from EE to SWE to ML research.

All of them are efficient in their own way, but the EE blows everyone else out of the water in sheer _effectiveness_. He may not be the strongest programmer, but his solutions have an elegant simplicity, take the right trade-offs and solve the damn problem.

The CS members are exact opposite: they care about the solution more than the problem, leading to hard-to-maintain / partial / sometimes outright wrong approaches. If they don't find the problem mentally stimulating, they redefine it to make it so, and then solve that problem instead.

Problem: display one point in an image

EE/SWE solution: calculate the xy pixel location and pass it to the renderer

CS solution: define a novel normalized coordinate space, so (0,0) and (1,0) are two specific locations in the image (not center, not corner, but two content-sensitive locations); for every image in the database, calculate a 4x4 "normalization" matrix to map pixel coordinates to normalized coordinates; now calculate a 4x4 "location" matrix with the location of the object in this normalized space; problem solved.

Note how this not only fails to solve the original problem, but it also creates multiple new ones.

Our team then had to point out that all of our user data, generated data, rendering code, user interface, user manual are standardized in pixel coordinates (_industry_ standard, with strict regulations), and that no, defining a new coordinate space, migrating terrabytes of data, and convincing the industry to switch over is not going to happen.

So yes, give me an EE/SWE problem-solver over a CS academic any day of the week!

[hedora]

The problem space changes every few years, though. What does the career progression of these "professional Software Engineers" with no understanding of the solution space look like? Do you just fire them each time the problem space changes?

What about tech debt? Are they writing shovelware or something? Over time, as requirements drift, how do they even know their stuff is way off of being a reasonable solution?

[kortilla]

> Over time, as requirements drift, how do they even know their stuff is way off of being a reasonable solution?

CS doesn’t even start to scratch the surface of reasonable solutions though. CS programs don’t teach you anything about architecture, technical debt, testing, source control, bug tracking, etc.

[feoren]

> CS programs don’t teach you anything about architecture, technical debt, testing, source control, bug tracking, etc.

Bug tracking you can learn in a day. It's more about which tool your company is using than anything fundamentally difficult to understand.

Source control you can learn in three days, and I was (briefly) introduced to git in my CS degree. How source control systems work under the hood with diffs, Levenshtein distance, etc. -- that's the kind of thing CS covers. A CS major understands source control way better than someone who's just been using it for a while.

Testing is more of a habit or practice than a subject to learn about. And you absolutely learn to test your own code if you don't want to fail your assignments, because the professor sure is going to test it.

"Technical debt" is a communication term that was invented to help business people correct misconceptions about how software engineering works. If you mean they don't learn how to make maintainable software, I agree that's not a focus. Maybe it should be. But of course nobody in the industry seems very good at that either.

"Architecture" is a tremendously vague term. Are you talking about large-scale, multi-system architecture? The MVC architecture? A web tech stack? Most of those things are either just putting together things you already know, or a subject that you can learn about in a CS degree if you want.

[kortilla]

> Bug tracking you can learn in a day.

No you can’t. You can learn how to use a specific bug tracking tool in a day. That’s not related to learning how to write/use/organize/prioritize bugs.

This is something that all of my junior engineers sucked at at Google and it took months of prodding to get them to write useful bugs and years before understanding how to prioritize bugs.

> Source control you can learn in three days, and I was (briefly) introduced to git in my CS degree. How source control systems work under the hood with diffs, Levenshtein distance, etc. -- that's the kind of thing CS covers. A CS major understands source control way better than someone who's just been using it for a while.

This is completely wrong. Knowing how a diff is generated is completely useless when learning how to use git, perforce, whatever. 99% of learning source control is about the concepts of that particular tool and how to manipulate them (e.g. branching, rebasing, squashing, merging, cherry-picking, etc).

> Testing is more of a habit or practice than a subject to learn about. And you absolutely learn to test your own code if you don't want to fail your assignments, because the professor sure is going to test it.

This is what someone who knows nothing about testing thinks testing is. Running code before you submit it is a specific type of test, and it’s a pretty bad one. Making code testable involves architecting your code in specific ways and knowing when to use fakes, mocks, functional tests, unit tests, fuzz tests, performance regression tests, etc.

To claim it’s just a habit or practice you follow just right before you submit is a joke. This is one of the hardest thing to train new grads on when they join a company. Part of it is they have notions like yours implanted by completely out of touch CS professors and grad students.

>Technical debt" is a communication term that was invented to help business people correct misconceptions about how software engineering works.

CS doesn’t teach people how software engineering works either. That’s the point.

> Most of those things are either just putting together things you already know, or a subject that you can learn about in a CS degree if you want.

No, you cannot reasonably learn about these things in a CS degree because they are completely uninteresting from a CS academic perspective so the professors don’t care about it. A professor who teaches you about the lambda calculus is just as qualified to teach you about making a maintainable and scalable service as the professor who taught you newtons calculus.

The whole point is that CS only has a small intersection with writing software at the leading tech companies and an even smaller intersection with writing software at normal businesses. It’s useful for distinguishing between new grads but it’s garbage compared to industry experience.

I say this as someone who got a phd on the CS side and then went to Google. They are just completely different universes.

[derefr]

I think you've misunderstood the terms I used.

By "problem domain" I mean the things that impose constraints — for a structural engineer, that'd be e.g. building materials, soil, weather, etc. The things that have tolerances.

By "solution domain" I mean the space of human ingenuity that we can apply in our designs, in order to make something possible that wouldn't be possible with a naive approach. For a structural engineer, that's things like "suspension-bridge cabling" (more general principle: tensegrity) and, I dunno, "flying buttresses."

The problem domain of building software — the parts that impose constraints — are things like what factors lead to robustness (or lack thereof) of a language runtime under production load; programming-language error-rate as a function of language-syntax UX design; evidence-driven software project scope analysis; the trade-offs involved in attempting to scale a process horizontally vs. vertically; the effects of state on ability to scale; etc.

Someone who understands these things knows how to engineer software, the same as someone who understands material tolerances knows how to engineer a structure. If they only know that, then they can't draw you a building (that'd be an architect) — but they can take that architect's blueprint, and tell you whether the building described by it will fall over, and whether there's any simple thing you can do to solve that, or whether you need to draw a different building.

But note that you learn the solution space naturally, over time, as you're exposed to the solutions people use in the field. A machinist will learn the tools of their trade as they run into them in the shop, and as other machinists demonstrate them, and as books refer to them, and as job-lots demand them. None of this requires academic rigor. It's just learning on your feet.

A SWEng might not be aware of the academic result proving some more-optimal data structure exists for something. Just like a machinist might not be aware of a not-yet-commercialized maser CNC lathe. But they don't need to be, either. Very rarely does solving novel problems require novel tools. You can build the part you want to build with the machines you already have in your shop, and maybe one new one you buy off eBay. You can write the code you want to write with your not-so-optimal data structures, and maybe one new one you find in an ecosystem library.

Every once in a while, getting things done might require you read a journal paper written by an academic. But let's be very clear: it doesn't take a degree in some field, to be able to read — and make use of! — journal papers from that field. We've got educational vloggers — people who perform on camera for a living — operationalizing stuff they saw in journal papers all the time! If they can do it, a professional in an adjacent field to the academic discipline can certainly do it.

[kuang_eleven]

Interestingly, I think I am as close to an example of your last point as you are going to get. I was a Physics major, and nearly all of my classes were focused only on the strongly theoretical part of physics. When I then joined a research team in experimental cosmology, I did lament that I never got any real instruction on research methodology, relevant statistics, etc.

It's surprisingly how little actual fundamental, theoretical Physics you need to know to do Physics! I'm not saying it's not important, the point of being a Physics major is not only to train you to be a researcher, but for the sake of the knowledge itself.

It's very similar to CS in that regard; almost none of formal CS is useful when doing software engineering, and when it is useful, the skills are in knowing to recognize a problem and how to research it, just like Physics!

[sjtindell]

Thanks for the input, a great example.

[warent]

It's a packaging / communication problem.

"Computer science" is a term that no longer fits the mental model of the general population's idea. Basically nobody is thinking "Computers? Of course! You mean the lambda calculus, Turing machines, how these theories relate."

Most People are thinking about about the internet, websites, games, apps, robots, and so forth. Indeed one can build all these things without any concept of how busy a beaver really is.

Then you have a smaller subset of people who (allegedly) understood what "computer science" is from the outset, and they turn their nose up at anyone who misunderstood what this "Computer science" was. Even worse, they often feel somehow superior. How dare you be ignorant?

Practicality and value: these things can exist outside the realm of dense theory. Anyone who says otherwise is trying to feel better about the years and/or money spent on a very challenging and painful degree.

[FactolSarin]

As someone who is self-taught as a programmer, I came into the field with a serious case of imposter syndrome. There was so much theoretical stuff I didn't know. Then I got into my job and it turned out it didn't really matter and my practical experience doing hackathons and personal projects set me up for a lot of success. There are times and I wish I had a better theoretical underpinning, but it's honestly pretty rare

[Alan_Dillman]

This. It has really split into two domains, but the terminology is often muddled.

It is like the difference between a person who pours concrete foundations, and a person optimizing concrete formulas. Society needs both, but the skill sets are different.

[xeromal]

Yup, I took CS and had to go through all the rigors that entailed, but I really ended up being a construction worker. I don't mind! Really! But I think if I could do it all over again, I'd take a software engineering BS degree where most of my time was spent engineering solid software.

I did take design patterns classes and such in college, but imagine taking 200,300, and 400 level design patterns classes and learning how to architect scalable systems in the cloud or on-prem.

Of course there would be programming classes too, but I think there's some room for a program that I'm imagining. Boot camps don't cover the engineering and architecture parts so it would be somewhere between a bootcamp and a CS degree where you're writing operating systems and big endean and Big O notation

[feoren]

Except society doesn't actually need both "people who can solve new and interesting problems in an automated way using computers" and "people who build only easy, normal, routine, well-understood solutions to known problems using computers", because the latter is called compilers.

[connectsnk]

>If you want to solve hard problems that haven't been solved before, pay attention to that math and those proofs.

You are right that these people will have a hard time later. What also needs to be understood is that paying attention to something in which you have no curiosity is hard. I never paid attention to Maths / Stats / Probability and now I am having a hard time trying to learn machine learning / AI. But now my curiosity for these technologies is dialled up all the way to 9 and in my free time, I am relearning all these concepts I missed.

And what I have discovered is that these concepts were easy. The university presentation of it was done without any motivation. It took a useful and easy subject of math and made it hard.

[ivansavz]

> the university presentation of it was done without any motivation. It took a useful and easy subject of math and made it hard.

Amen!

I like the way you have worded this. In particular for first-year math courses, they are super useful and should be seen as "basic science literacy" and much more people should have access to this knowledge (not just people who take 3-4 years of courses in a STEM major). I've been working on products to make this happen. Links in profile.

The "basic science literacy" I can see everyone benefitting from (in particular devs): (1) math modelling skills from high school math functions, (2) vectors because EVERYTHING, (3) PHYS101 (mechanics) for the predictions-using-models skills, (4) CALC for understanding concepts of rates of change and accumulations, (5) PROB because important building blocks for modelling data, and (6) STATS so you learn how to infer model parameters from data.

It's not a coincidence the above list of 6 are part of most UGRAD degrees (either in first or second year). These are the basic tools that everyone benefits form knowing. I am really enjoying the "unbundling" that is happening of the basic science literacy teaching and the credentialism.

[thrwy_ywrht]

>But know that after a few years your skills will be out of date and you will have a hard time keeping up with the field

This totally depends on what type of engineer you want to be. It's quite possible to be very successful, have a great career and make a ton of money as a software engineer without ever tackling problems that are "hard" or "new".

[otikik]

The two alternatives that you are presenting (flavour-of-the -week JavaScript versus multiple semesters of Big O notation) are just the two extremes of a wide spectrum.

What you call “shallow”, others might see as “practical”.

It’s possible that this person has chosen study path that isn’t the right one for them. That’s a tricky spot to be in especially in these times. I suggest a little more empathy, and a little less venting.

[bdlowery]

I love spending so much time doing Big O analysis! Great use of my time! I’m sure I’ll need to know semesters worth of Big O Analysis for my job.

Oh… I just need to know the basics? Damn, such a great use of my time.

CS degree in a nutshell.

[urthor]

How often do proofs come up in industry though?

Usually I find proofs are what you bring in the consultant for.

I'm not going to hand on heart vouch for anything like that as a generalist.

Combinatrics, Big O and set theory absolutely. Everyone is far better off with those.

[mcguire]

Computer programming is applied formal logic. No, really, it is. I'm completely serious.

[hnfong]

You can reassert this tautological statement all you want, but when AI-assisted programming tools start compiling pseudo-natural-language into C++, you'd better accept the fact that either:

1. The definition has no bearing to what's happening in the real world, or

2. "Computer programming" ceases to exist as a productive activity and you need to invent a new name for AI-assisted programming.

[mcguire]

But that is part of the point: there is no programming tool to compile natural language into code. Instead, a programmer has to convert the natural language into a formal language that a compiler can deal with. You know all those nifty refactoring tools? They're treating the program as a construct in a formal language---they can make specific changes without altering the meaning.

Oh, and there is nothing tautological about it, at least as far as most programmers seem to work.

[hnfong]

> no programming tool to compile natural language into code

Pretty sure you missed a couple news articles recently...

https://news.ycombinator.com/item?id=30179549 https://news.ycombinator.com/item?id=27676266

They're not exactly reliable, but you probably could say the same for the earliest compilers (from programming languages to asm/machine code).

I'm not saying they will definitely be usable in the short term future, but that future is probably coming sooner or later, and I don't think a fragile definition (programming==="applied formal logic") is worth reiterating over and over again as if it were some fundamental truth.

[MaxBarraclough]

I don't think anyone is going to dispute that though, except perhaps on a point of detail: it took Hoare logic to bridge the gap between imperative programming and 'ordinary' mathematical logic.

If I'm reading them right, urthor's point is that the average programmer doesn't directly benefit from being skilled in developing formal proofs about code. (I rambled at some length on this topic recently. [0]) Very few software development workplaces value correctness so highly that they invest in formal methods.

That said, I think the case can be made that learning about formal methods is useful in instilling a sense of how rigorous software development can be, and perhaps to develop a healthy contempt for hand-wavy sloppiness. Perhaps it's also helpful to learn that informal requirements, formal specifications, and implementations, are three different things. I think this may be true even if we rarely use formal specifications in practice.

[0] https://news.ycombinator.com/item?id=30000146

[roenxi]

Breathing is applied formal logic. The range of things that can be reduced to applied formal logic is pretty much everything if we accept that "applied formal logic" doesn't mean formal mathematical proofs.

If we could strip out of reality the bits that can be understood as a practical application of the basic branches of math there wouldn't be anything left. Nevertheless most people get a long way in life without needing to engage with that (which is lucky because there is too much to learn in one lifetime).

[baq]

I get the OP’s point: it doesn’t matter what you think about usefulness of knowing how to formally prove anything, the point is that you’re doing it every time you program a computer, so might as well 1) be aware of that 2) learn a thing or two about how it’s done by pros.

[roenxi]

Should I study knot theory to tie my shoelaces? Should tailors and cup-makers study topology? Should it be mandatory to study economics before buying groceries?

Looking at code as applied formal logic is not a useful view outside of some rather obscure communities. Even code as a recipe is more useful view in practice.

[baq]

I’m not saying you should get a phd in logic. I’m saying knowing what your inputs and outputs are supposed to be and writing test assertions is basically checking whether your theorem/lemma is true in disguise (unit of code works as expected) and knowing a bit of theory from that domain can’t hurt. Even if it’s only de Morgan’s laws, which you’ll admit is a rather low bar…

[mechanical_bear]

Computer Science isn’t about industry. If you are dead set on being the best in industry go take an Information Systems degree, or similar.

[urthor]

I think if computer science degree weren't about industry, there'd be far fewer terminal undergraduates floating around the world.

If people came for the science, everyone would ride off, discover something, and get a PhD.

I just see formal proofs as something you get a genuine scientist to do. If you're someone focused on rigorously correct proofs, you get a rigorous PhD.

I don't pretend that I'm up for that, or that I'm qualified to produce quality work in that space.

But I also don't believe any of my fellow terminal undergraduates are the right sort of people to do this work.

Let's face it, we all had a close encounter with the mathematical proofs, and ran in the other direction as fast as we could!

[mechanical_bear]

I didn’t run, I double downed lol. CS Piled High and Deeper here…

[ajsnigrutin]

Yep, I look at this as a builder vs an engineer.

A lot of modern entry-level programming is the same as builder-work... take a brick, take some cement, spread the cement, put the brick in the right place, and in the corners, cut a brick to size. Yes, sure, we need a lot of those people doing random programming jobs too.

But, if you want to build something bigger than a doghouse, you also need a lot of math and calculations, before you even touch the first shovel, to calculate if the whole project is even theoretically feasable. Stuff that works in low scale, sometimes breaks horribly, with larger amounts of data, and i'm not talking facebook scale, but going from 10 to 500 users. If you want to go higher, things become even more broken for someone who just "lays bricks", and a lot more thought and math is needed to make things actually work (and scale).

[mechanical_bear]

Exactly! Computer Science is the study of computation, not the study of programming. Sometimes we use computers and programming in order to better understand computation. Just like Biology is the study of Life, not the study of how to use a microscope better.

[fivea]

> Math, big-O, and proofs are programming concepts. If you want a shallow understanding of whatever (...) take a coding bootcamp. But know that after a few years your skills will be out of date and you will have a hard time keeping up with the field.

I'm sorry to break it to you, but your personal belief on the virtues of ivory tower feats doesn't hold any water in the real world. At all.

The most important competency, by far, is being able to onboard. Whether it is onto projects, frameworks, programming languages, architectures... Being able to jump in and get up to speed and fix things and implement features is what matters.

No one cares at all if you know an algorithm by heart. Plenty of critical services are built upon crude O(n²) brute force implementations that are good enough, and no one bothers to waste 5mins to even switch the underlying container.

You're talking about a field where premature optimization is recognized as one of the worst and most fertile sources of problems. And who exactly is behind this problem? Precisely these short-sighted theorists, who believe big O musings has critical importance when it has close to none beyond superficial analysis of "should I use an array, a linked list, or a hash set"?

I know people with a boot camp and experience with a framework who landed jobs in FANGs, and I know PhDs in computer science that can recite inconceivable algorithms who can't get a job in the industry. How do you explain that, if waxing lirically about computer science is supposedly so critical?

[heavyset_go]

I know it's en vogue to shit on boot camp students, at one point I did as well, but my experience with working with such students is that after a few years, they are on par with their peers who studied CS in college. Yes, they probably won't work in research roles or roles that require heavy math skills, but when it comes to your typical software engineering role, they're fine.

Also, runtime analysis isn't that difficult of a skill to pick up.

[pessimizer]

I don't know that they're shitting on bootcamp students. I've taught people how to program who later became productive working programmers. I couldn't have taught someone all the stuff I learned in CS. It would have taken forever and I'm not smart enough. I do hope the people who wrote the low-level libraries that the people I taught use went through a CS program, though.

Runtime analysis isn't that difficult of a skill to pick up if you have a decent university-level math background. You can be a productive programmer without that.

[hnfong]

I'm not saying you're wrong, but... I'm just sick of narratives like yours that basically encourages "pure maths people" taking over "computer science" departments and pretending that their work has "real world applications" on the one hand, taking advantage of the tech boom in recent years, and on the other hand claim that CS degrees are only for research purposes and you industry people asking for job relevant training can bugger off.

It's a really narrow mindset to put math specifically on a pedestal. A lot of hard problems with computers don't involve heavy pure math. A lot of those problems get categorized as "software engineering" and as such it is often claimed not relevant for "computer science". But given the importance of software in today's world, academic institutions seem woefully disinterested in setting up "software engineering departments", and woefully disinterested in promoting "software engineering" degrees as an alternative to the typical CS degree as a entry ticket towards a software engineering career.

You must learn this (mostly) useless skill to do enter a profession that where you're probably not going to use the skill. It's classic gatekeeping.

You might argue that research universities are not supposed to be vocational colleges, but that's a hypocritical lie too -- they basically have to be, otherwise they'd be out of an important source of funding (tuition). The existence of bootcamps are evidence that these fancy "math" people pretending to be computer scientists are basically incapable of teaching anything useful to people wanting to learn to program computers. If bootcamps are so trivial, why couldn't universities offer (for example) summer courses that do the same thing? We're not talking about CS majors here -- we're talking about non-CS non-math majors who might want to learn more about programming. Is it reasonable to force feed them CS type pure math as well ? (read the parent posts again -- Quote: 'I went to the University of Waterloo and took a "Intro to CS for non-math students" course.'

Physics majors are called physics majors, not "telescope science" majors. If there's a "telescope science" department I expect them to teach, in addition to theoretical physics, practical courses on how to operate telescopes. My not so humble opinion is that CS departments are a misnomer, but they kept the name because CS degrees are popular, the field is flush with research moneys, and they're happily eating the profits from the software engineering cake while having their math cake too.

The pure math specialists in CS departments churn out starry-eyed students who in turn perpetuate the myth that CS is (only) math, and the impression that hard problems in software engineering is not a worthy intellectual endeavor for a research university. This attitude is going to hurt us in the long term. No amount of strawman arguments about ReactJS bootcamps is going to change this fact. Those bootcamps are evidence of a total failure of academic institutions to actually do research on and teach software engineering.

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In case it matters, I learned all those Big-O and algorithms shit in high school, and I'm not criticizing it based on ignorance of what they are and how useful they are. If anything, those concepts are too trivial to deserve so much "screen time" in the curriculum. I have friends who work in CS departments and publishes on FOCS (you know what it is, right?) etc. I'm reasonably informed about what I'm talking about, and I'm aware that many CS researchers just happen to like to research on math-ish topics (which is of course not their fault). But what I'm trying to say is that there is a fundamental, institutional problem with people snobbishly brushing off real world software engineering problems as if they are somewhat inferior. Get off the high horse already. You don't really need to learn the concepts of limits of sequences to infinity to count 3 nested for loops and know that maybe it will be slow for large inputs. Math will actually not tell you how slow it will be -- FYI sizes under 100 is usually acceptable for O(n^3). Claiming that "trust me, this math thing is so much more fundamental" is a really poor excuse for teaching (mostly) irrelevant concepts while pretending the degree is relevant to industry.

And yes, I don't have a CS degree because I already saw through this bullshit 20 years ago when I was in high school. I made sure to learn the stuff I needed to know and skipped the kool-aid. Got a degree in law (it's an undergraduate degree here), and surprise, I actually learned a few things about law -- and they didn't shove pointless math down my throat. I mean, if they wanted to, they could model precedents as an directed acyclic graph and make a couple theorems out of it, right?

[seba_dos1]

...but CS is math. A branch of it, to be exact. A lot of hard problems with computers don't involve CS, and vice versa. What's more, in many places there are separate CS and IT degrees that you can pursue.

Your claim about gatekeeping is later contradicted by the fact that you actually didn't have to obtain that degree at all. I didn't have to either.

I do agree that "Computer Science" is a somewhat misleading name though. It's pretty much as if we called astronomy "Telescope Science" and then wondered why people that come studying it expect to learn about building telescopes, with others arguing that you need to know a fair share of physics in order to build a good telescope anyway (which of course is true, but...).

[hnfong]

Yes. But also, names are important. CS used to be mostly math for historical reasons. But it doesn't have to be that way, and we have actually solved a lot of the math problems in these couple decades (P!=NP is, of course, nowhere in sight...). We've found a lot of new problems that don't necessarily involve math, and I don't think we should invent a dozen more new names for these fields just for keeping the historical baggage "pure" for maths. I think we probably need to ask ourselves, if CS really is (and should be) just maths, why not just do all the CS research in the maths department and make CS do something that actually relates to real world computers and computing?

[yellowcake0]

> ...but CS is math. A branch of it, to be exact.

And biology is just self-replicating organic chemistry.. and chemistry is just the physics of outer electron shells.

[ALittleLight]

If someone is studying physics to become a physicist they would be a fool to complain about learning all that math. If someone is learning physics to be a telescope operator then it seems like they have a reasonable complaint.

[peterangular]

This is exactly why I think the distinction between computer science and computer engineering is incredibly important.

[chaosite]

Yeah, Computer Engineering is that twilight area between Computer Science and Electrical Engineering where you care that your algorithm is PSPACE-complete but also need to know how it's affected by parasitic capacitance, right?

[anfilt]

Lol made me laugh, that is one way to say it.

[anfilt]

I went to school for computer engineering. It's a hybrid of EE and CS. It's not a not a programming only degree.

[sizzle]

Very articulate, explained what has been on my mind for a while. Boot campers need to level up their learning to compete in the future.

[overkalix]

Amen brother.

[throwawaynay]

Math is math, not programming.

No it's not about react or ror.

It's about solving actual real life problems. 99% people don't do CS to do academical research, but to work on life change products(and that's more likely to come from user research and fast prototyping, than from theoretically perfect fundamental research).

Some people build millions dollars software products, solving real life problems, without having a clue what big-O, quadratic functions, or even a lot of actual theoretical programming concepts are. And I personally know a bunch of those people. Some people know almost everything there is know about CS theory and never build anything useful to anyone.

Physics have literally nothing to do with CS or SWE. I think this is just pointless elitism.