My issue with this article is the author's conflation of his own like and dislikes with what works for students. Many people go into computer science because they want to know how computers work or because they want to know how to program that demo they just saw, or more applied things along these lines. Very few (but definitely some!) go into computer science because they want to know about Turing Machines, or the Curry-Howard isomorphism, etc. Currently CS education serves the latter group very well, while neglecting the former group. This is a shame, because plenty of people who find math frustrating can become great programmers and find great satisfaction in understanding programming and computer science better. Many of the most interesting fields of computer science aren't very mathematical.
When I came to school I hated math, despite being relatively good at it. By now, my interests have changed and I'll graduate with a minor in math and a major in computer science, but largely in spite of my experience with collegiate mathematics classes rather than because of them. The way to get people to appreciate math isn't to force them to take classes on something they don't find interesting. It's to give them opportunities to see how it can be interesting or relevant to them and let them come to it when they're ready.
For myself I became more interested in math when I wanted to learn about graphics programming and machine learning. The first requires linear algebra and the second requires a lot of probability and statistics, which themselves require a solid understanding of calculus. By coming to mathematics this way I was able to see it's relevance to me and my interest in it grew the more I learned. Previously, when I was forced to take the standard multivariable calculus -> linear algebra sequence I did just enough to get by, since I really didn't care about the material. Once I had this small positive exposure to math that was useful to me it was much easier for me to appreciate more abstract math as well. But the important part is that no one made me learn this stuff! Had this been part of the curriculum I would have hated it, but since I was allowed to learn it when I was ready for it I found it very beneficial.
> Many people go into computer science because they want to know how computers work .. Very few (but definitely some!) go into computer science because they want to know about Turing Machines
I'll be a little provocative, but students don't necessarily know what is useful for them. It's like those kids that want to learn to play guitar solos like Eddie Van Halen but refuse to learn any music theory.
Besides, when you look at the recruiting process of some prestigious companies, it seems that industry values employees that can show good mathematical skills. It's not simply an academics thing.
>Besides, when you look at the recruiting process of some prestigious companies, it seems that industry values employees that can show good mathematical skills.
Industry usually values going in-depth on algorithms, data structures, and not much else. I've occasionally worked at or interviewed with companies who consider knowledge of things like Curry-Howard or Turing machines interesting side diversions, and very rarely seen companies that actually use such theoretical knowledge (functional programming shops or compiler houses, usually). You'd think ML theory would be very useful, but much of the time, ML implementations are trained off-the-shelf to just fit a linear model of some kind to whatever-the-hell data you've got -- and if it can't fit a linear model, fuck it.
For what it's worth, the education that Devlin is suggesting is not theoretical computer science or type theory. It's the more traditional mathematics courses.
Do you have any example math questions during recruiting?
I have seen more discussions about CS theory questions in recruiting. And this is still only for a few jobs most companies only care about your programming skills (and soft skills like people skills which maths doesn't help at all).
The counter is that a student (in part) wastes the graphics class because he/she did not have a solid foundation in the prerequisite topics. I was shocked how many of my friends in graphics were programmatically "multiplying matrices by hand" over and over again instead of relying on the abstraction. I agree with Devlin here: it's a cultural difference that they don't consider mathematics "as real" as they do programming, and their programs suffer for it.
So maybe what CS departments need is a class that surveys the higher-level courses, and motivates the foundational classes they will need to take for each subject.
Many people go into computer science because they want
to know how computers work or because they want to know
how to program that demo they just saw, or more applied
things along these lines.
Then they're in the wrong place. They should download a copy of SICP and a Lisp interpreter, and get busy hacking. Read open source software, contribute to a project that you like. Get a job, be productive.
A music student doesn't go to university to learn how to play an instrument - he first becomes a musician, then he goes to university.
A good metaphor that I like to use is to relate computer science as it is now to physics, engineering and maths. All three have a degree of overlap, but each of them thinks about the same problem entirely differently, with different purposes. The mathematicians try to build abstract formalisms, which physicists then apply to explain real-world phenomena, which are then exploited by engineers to build artefacts.
Of course, that's a huge simplification, and in reality there is huge overlap - mathematicians often perform the "modelling the real world" aspect, physicists often work with highly advanced abstract mathematics (general relativity anyone?), and engineers often do very fundamental research into physical phenomena.
A similar hierarchy can, and should (I believe) be applied to what I'll call the "computing sciences": Maths remains where it is, while computer science replaces physics, and software engineering replaces general engineering.
Under this model, it would seem ludicrous for someone to study computer science and complain that they were not being taught to build programming artefacts. A student studying physics would be laughed at if they complained that they weren't designing enough cars, or building enough bridges.
I once heard someone opine that computer science education plays a cruel joke on its students, and this blog post is perpetuating that joke. Computer science is often treated as an offshoot of mathematics when, in reality, you need to do far, far more writing than advanced math. Most students are going to be leaving school and working on professional programming, where they'll largely work with a group of people jointly maintaining systems. And these systems require good writing skills to effectively write documentation for the code, as well as writing the design documentation.
In contrast, you'll rarely need more than algebra, basic statistics, and maybe graph theory most of the time. All the advanced math stuff, such as SVM or Taylor expansions, are generally going to be found in the libraries that you'll use rather than code you'll write yourself. You may need math to understand what they're doing in detail, but with better writers on the documentation, you wouldn't need the advanced math to understand how to use them properly.
When I was in college, I was once not allowed to join a Complex Analysis course because there were 15 students in the class. College guidelines advise against more than 12 students in a writing-intensive course, and because this was an upper-level Mathematics course, the professor took that to heart (he thought 15 was too much, but some needed the course to graduate).
This is to say that proper abstract Mathematics courses should require the students to write many proofs. I'm sure that students in the aforementioned Complex Analysis course were writing 4 - 7 pages of mathematical prose for their weekly problem sets. This is a non-trivial amount of writing practice which is especially tuned toward accurately expressing the interplay of precisely-defined abstractions (which all documentation should strive for).
Even discrete mathematics courses (Combinatorics, Graph Theory) should eschew simple calculations of permutations/combinations and graph traversal algorithm steps in favor of writing proofs of more of the abstract concepts. In this way, students will be trained to write effectively about abstract concepts, which will prepare them for a career in programming as well.
I'm not sure that type of writing is anywhere near what you need to do in common programming jobs. As you wrote, it was "4 - 7 pages of mathematical prose...". I interpreted writing as writing text to others, including non-programmers.
>Computer science is often treated as an offshoot of mathematics when, in reality, you need to do far, far more writing than advanced math. Most students are going to be leaving school and working on professional programming, where they'll largely work with a group of people jointly maintaining systems. And these systems require good writing skills to effectively write documentation for the code, as well as writing the design documentation.
Computer science is an offshoot of mathematics— or it was until everyone decided that "computer science" meant "software engineering."
It seems like you didn't catch the point of the article, which is not that the math is useful in and of itself, but rather useful in training one to juggle abstractions.
First of all, I want to say that this industry could do with a much higher level of mathematical rigor, and that a lot of the systemic problems are owing to the lack of it. But to me, this:
> It was a good discussion, that highlighted the distinction between the currently accepted view of mathematics as primarily about properties and relations, and the pre-nineteenth century view that it was at heart procedural.
...does not follow. Programmers don't view mathematics as procedural because they studied mathematical history, but because the Von Neumann architecture, from which the semantics of almost all programming languages derive, is highly stateful and procedural.
As I see it, this is the core problem that must be solved before many of the tools of mathematics which would really help us today can be meaningfully exploited.
Mathematics is used in education (schools, ..., universities) to weed out people. For those who perform this selection the ability to grok mathematical problems often is the only way to categorize human capacities. A self-fulfilling prophecy.
Exactly. Even the author of this post has argued for "filtering out" the weaker students in a Darwinian fashion with MOOCs:
"MOOC education is survival of the fittest. Every student is just one insignificant datapoint while the course is running. Do well, do poorly, struggle, drop out – no one notices. But when the MOOC algorithm calculates the final ranking, the relatively few who score near the top become very, very visible. Globally, talent recruiting is a $130BN industry. It’s “Google search for people” in action."
In contrast, there are folks who look at how can we help more students succeed in math and physics (without dumbing the courses down and grade inflation). Wright State (and now many other universities) created a pre-calculus course that teaches the math in context with many engineering examples. 90% of students who took this course went on to pass Calculus, compared to 60% of those who didn't: http://cecs.wright.edu/community/engmath
The object is to teach math and science. Prestige is a side-concern for the kinds of, how shall I put this, foolish mortals who believe that zero-sum rankings make them special, eve when those rankings fail to represent an objective increase in their knowledge and capabilities.
When it comes to scientific knowledge, it is very definitely better to serve in Heaven than to rule in Hell, so to speak.
I can't agree with the "mathematics used to weed out" terminology, the weeding out may be an incidental part, but I've never encountered a place where mathematics was used to diminish the number of people following a pursuit. Certainly not the way that Organic Chemistry is used to weed out premed students.
And certainly not in Computer Science, as mathematics is avoided in many cases where it would greatly aid in reasoning about systems, due to the quantity of people who dislike it.
And in Physics, mathematics isn't used to weed out people, but to train them on the theories that underpin the entire field.
I'd be interested in hearing where this happens, because it's certainly not been my experience.
"... as mathematics is avoided in many cases where it would greatly aid in reasoning about systems, due to the quantity of people who dislike it."
Computer science at Penn has been doing this especially. In the overview of mathematics course at Penn, any mention of algebra has been removed. They used to have a section on toy RSA--deleted. It seems like they want to remove all mathematical rigor here, just to turn out more people with a CS degree. People can hardly walk away doing a proof, like the showing how a greedy algorithm such as Kruschkal's can work. To see this for me is frustrating to say the least. So I must concur.
Side story. The other thing is, CS has nearly doubled since I attended, 4 years ago, and they have NOT hired faculty to keep up. Class sizes are getting ridiculous.
Calculus is a requirement for all undergrad science degrees in most places, and a biologist has about as much use for integral calculus as a fish does with a bicycle. They're much better served by statistics, which is not, a majority of the time, an undegrad requirement for all majors.
There are large areas biology that depend crucially on calculus and other math. Genetics as a field is extremely quantitative. Ecology and evolution also rely heavily on mathematical models. You can get by in some parts of molecular biology and field biology without a great understanding of calculus, but even in those subfields its important to have an undergraduate-level understanding of genetics, ecology, and evolution.
At my university in germany, students had 3 semesters to prove themself. They had to pass certain exams. Otherwise they get kicked out. They gave you 2 maths courses, of which you have to pass at least 1 exam. Math was the game changer for most students, who did not make it.
It's been used for exactly that purpose at one particular university I'm highly familiar with. Certain departments (e.g. Psychology) didn't want to deal with the hassle of flunking their students out, so they outsourced the job to us (math) by requiring calc 1.
"Weed out" is a euphemism. The purpose isn't to diminish the number of people following a pursuit, it's to ensure they have the analytical aptitude to be successful at it.
Quite a few competitive major programs where I got my degree required students to complete 1st year calculus (differential, integral, multivariate) and some or all of 1st year physics (mechanics, electromagnetism, waves, optics) before applying.
I know at my Alma Mater, University of Maryland, all the comp. sci majors had to pass calc 2. I knew of some people retaking one of those courses 2 or 3 times trying to pass. UMD is generally considered a top 10 comp. sci. school. Just anecdotal evidence though, I'm not trying to argue against you.
It was pretty well-known, and it was really obvious because at most schools, calculus is three semesters, while UTD condensed it down to two without reducing the material at all.
The University of Waterloo's unique by putting their Computer Science program in our Faculty of Mathematics (the first in North America, the largest in the world).
By studying a significant amount of Math, it definitely improved my logical and reasoning skills at least ten fold.
Computer science is a branch of mathematics. If you mean engineering and programming, then you can get by without any math, but I don't expect you to be writing compilers, operating systems, database engines either without solid CS education.
Compilers don't require much math. Nothing difficult at least. (I guess if you're interested in compilers for functional languages this might not be as true). Same for operating systems, as far as I know. I have no experience with database engines. CS theory on the other hand is very mathematical, as are some more applied subfields such as cryptography or graphics.
Math is nice, and useful for some classes of problems, but it is certainly not needed for huge amounts of the programming done. Some people have this masochistic ideal of programming where if you're not solving problems that require the most sophisticated mathematical techniques then your problem isn't important. There are plenty of problems in programming that aren't particularly mathematical in nature and are very challenging. Compilers are one of these, as are many others.
It really depends what is meant by "math". Not everybody has the same perception of what maths are. I used to teach a language theory class where students had various backgrounds. CS students were bored because they don't like maths, and maths student were bored because it wasn't math to them.
Concerning compilers, they require non trivial maths. Parsing, type checking, code generation, register allocation and so on... The thing is that mostly you don't formally prove things like you would do in a maths class. But indirectly you use a lot of mathematical results.
To my own amusement, I've discovered that I tend to categorize as "math" those areas of mathematics which I struggled with, or am ignorant. Statistics? Differential equations? Math. Doing a DFS walk over a parse tree in order to do code-transformation optimizations? No math involved! Just some code.
Compilers (at least optimising ones) bloody well do! For example discrete maths/graph theory for control/data flow graphs or optimisation algorithms for register allocation/loop unrolling.
Or, if those don't count as maths to you - look up the polyhedral model: linear algebra applied to loop tiling/parallelisation.
This is true. But a very low percentage of practicing software engineers, software developers, or web developers actually write compilers or operating systems. The demand today (e.g. what most companies need in terms of software talent) is pretty far removed from these activities. I believe this is what is driving the emergence of code schools and online software development programs.
You can get by without math in engineering? This is news to me... I think what you meant is: you can use existing hardware and software tools to build software applications and systems without relying on "advanced" math. I would say this is the point of building hardware and software -- so other people don't need to learn how to do it. They can just "use it"
I assume you mean you're confused on being downvoted. I did not think a downvote on your comment was warranted, so I gave it a corrective upvote.
In general, it's not worth worrying about one downvote; that one person did not like your comment enough to downvote it is a rather weak signal. There are a lot of users on HN. Other users (such as me) are also likely to give you a corrective upvote. It's when you get multiple downvotes that the signal matters.
A practical math topic I think all computer science (and engineering, really) students should take is convex optimization.
Often a problem in engineering or computer science can be phrased as minimizing a cost function over some data and/or variable constraints. Take SVM for example, or sparse signal denoising, or template matching. And far too often, people will apply "advanced state of the art techniques" to solve these problems, e.g. neural networks of some exotic kind, graphical models, etc. And while these are great techniques, they need not be applied to convex problems of a reasonable size.
In the examples I listed, I can almost guarantee that a knowledge of how to phrase the model as a convex optimization problem and throw it into an interior point solver is going to be much more effective than running stochastic gradient descent on some user-defined model with way too many (or too little) parameters.
Because when you say a problem is convex, what you are saying is that it can be solved globally in polynomial time (usually on the order of ~20 least-squares problems of size corresponding to number of variables and constraints). There is a sophisticated convergence theory as well. And if you understand the mathematical theory, you get to use this mature technology to solve exactly a whole class of problems you wouldn't think you could.
It's also worth mentioning that a lot of very hard non-convex problems can be approximated and solved well using these methods, and this is a rapidly growing field of research. Here's an example: http://papers.nips.cc/paper/2979-efficient-sparse-coding-alg...
Not that you stated to the contrary, but the reality is that the theory and algorithms for convex optimization doesn't really differ that much from the theory of general nonlinear optimization. Basically, we get different guarantees, but we're still largely applying the same algorithms (Newton based methods combined with things like projection, active set, or interior point methods for inequalities.) Though, I very strongly agree that knowing convex optimization tricks helps this process immensely. If you're going to formulate a problem, please, dear god, choose the convex one if given a choice.
Mostly, this is a long way to say that if we're going to study optimization, we might as well study continuous optimization, which encompasses convex optimization and isn't that much more difficult.
And, yes, this stuff is incredibly useful with applications in most fields of study. I've made a nice career out of it.
The author conflates abstract and immaterial. Computer programs don't have to be abstract, and often are not. And that's precisly what makes them more approachable than mathematics: you manipulate actual structures, see the results in real time and get to see how the actual process is carried out without language ambiguity. In short you get a concrete feeling of things that you can build a mental representation of that's closer to the senses.
Now some software engineer should explain to the author how programming is central to his understanding of mathematics and see how things develop.
Computer programs are formal objects, just like let say Euclidean geometry is. Their syntax and semantics is entirely abstract and mathematics, even though they can be run on a machine that gives you a concrete feeling.
And conversely, most maths aren't that abstract. Mathematicians constantly draw diagrams, plot curves, and use various concrete representations of the object they manipulate. Geometric intuition is a very important tool. I wonder if it's not our best strength compared to machines.
> And conversely, most maths aren't that abstract. Mathematicians constantly draw diagrams, plot curves, and use various concrete representations of the object they manipulate. Geometric intuition is a very important tool. I wonder if it's not our best strength compared to machines.
That would fit with the whole gestalt theory and how our brains are really good at intuitively* finding shapes within shapes (literally).
Perl and Javascript programs are not formal objects, anymore than an essay is. Any language where "the implementation is the spec" is not a formal object.
Software development helps me understand 'kinds' of information, and that the applicability of mathematics is dependent on the kind.
But mathematics is not a sense, and neither is a program. These are the best conceptual constructions we have to represent our senses as humans collectively. They are both meant to be representations, models. This is something I continue to find difficult to understand, from both mathematical and computational perspectives, because computational and mathematical concepts are intuituve to me. However, I have the awareness that they shape how I naturally parse the raw data I get from my senses. That means it's important to let go of either model when it ceases to accurately represent reality.
"Once you realize that computing is all about constructing, manipulating, and reasoning about abstractions, it becomes clear that an important prerequisite for writing (good) computer programs is an ability to handle abstractions in a precise manner. Now that, as it happens, is something that we humans have been doing successfully for over three thousand years. We call it mathematics.
This suggests that learning and doing mathematics might play an important role in educating future computer professionals.
"
The conclusion is not good. The logical conclusion would be that if we define mathematics as 'handling abstractions in a precise manner' then programming is part of mathematics.
In this case his conclusion becomes:
'learning and doing some classical parts of mathematics might play an important role in educating people who will do this another part of mathematics'
Which might be true to an extent, but this statement is very broad.
It is not a question that some amount of math is helpful. But after a while there are diminishing returns, because you simply learn different skills than what you need.
After having some very basic math knowledge the best strategy is to try to learn a lot of practical 'engineering' from very good books/courses, and only go back to math on-demand.
For example if you want to have some knowledge on machine learning or computer graphics, don't start to learn math for years first and then start learning about machine learning/3d graphics. Start to learn abut the engineering field first (take Andrew Ng's coursera class, or some equivalent in 3D graphics) and only go back to learn some linear algebra or very basic calculus after you see why it will be useful and to what degree you need it (you mostly don't need most of the theorems if you are learning the basics of these engineering fields).
Being an engineer is very different than being a mathematician. If you want to be a good engineer then mostly learn a lot from good engineers (or researchers in the engineering field) and learn some math along the way, but that will be secondary in my opinion.
It's good to learn tons of math, but only if you learn even more engineering besides the math (to be a good engineer).
His words: This suggests that learning and doing mathematics might play an important role in educating future computer professionals.
Your words: some amount of math is helpful.
Do you not see how these are the same idea, abstractly?
Also,
"if we define mathematics as 'handling abstractions in a precise manner'"
That IS the definition of mathematics. The author is not arguing for more calculus. His background is mathematical logic which is a standard course before all "real" math (analysis, topology, abstract algebra). All of these subjects handle abstractions in a rigorously precise manner.
But if programming (computings as he writes) is handling abstractions and math is handling abstractions then programming is math and the argument that we need to do more math is unclear. We could just do programming, he wrote that it basically is math. He needs to be clearer in what kind of math he thinks would be helpful to programmers and why it would help more than "normal" programming/computing courses.
> He needs to be clearer in what kind of math he thinks would be helpful to programmers and why it would help more than "normal" programming/computing courses.
Here's a crazy idea... let's stop treating curriculums like silver bullets and realize that no matter what we do we're not going to succeed with the current system of "hope your innate talents match up with the current education model du jour".
Education needs to be radically tailored to individual students or we're going to continually be trapped in this vicious cycle of "Current education paradigm X is broken, paradigm Y will solve all of our problems!" (where Y ∈ {"more math!", "more programming!", "more fundamentals!", "more application!", ...}).
Yes, highly customized education doesn't scale. Yes, it's very expensive. But sacrificing future generations on the altar of "efficiency" seems highly suspect to me, and the honest fact is if the incentives within the American (sorry non-US people!) education system were properly aligned we would be much farther along. Granted, that's a difficult task, but I didn't get into computers so I could spend my time working on easy problems.
> Some of the computer scientists, however, instinctively interpreted the statement “Let x be such that P(x)” as a variable declaration. This led them to give an “incorrect” answer to a question
"Let x be such that P(x)" is something like a variable declaration. It's the introduction of an argument to a function (think "function(x){ ... }"). I think the real conceptual problem lies in the fact that many mainstream programming languages allow subversion of the type system by allowing "uninitialized" variables, so that every type is automatically inhabited.
There is also the fact that a mathematician may just want some "x such that P(x)" rather than introducing a generic one, which I think the author mentions.
This is a quibble, but there are cases when the physics of the computer cannot be disregarded and must be considered. Two of my favourite examples are cosmic rays flipping bits [1] and printers jamming. [2]
Regardless, I agree with his overall thesis: the machine is just a hunk of atoms with no meaning of its own. It is us who place a meaning on the machinations of the computer. Computer people have come to accept the abstractions of computer science as tangible and consider the abstractions of mathematics intangible. I think both abstractions are comparable. Both end up being interpreted and executed by humans and by computers, and it is merely the overwhelming prevalence of CS abstractions being executed by computers and mathematical abstractions being executed by humans that
is causing this divide between the two tribes.
There is much that computer scientists can learn from remembering that the machine is just a represantion of abstractions, just like mathematicians can benefit from remembering that occasionally the physics of the machine are relevant.
I really enjoyed this article and the comments so far. I struggled with math heavily in high school and in college and believed I wasn't fit for programming despite being a 'computer head' since I was a kid.
I never struggled with statistics, I felt like it was the 'easy' math and now work in analytics at a large investment firm and do scripting and analysis with the big databases.
I don't think I'd excel at creating the databases I work with but I understand them and my ability with java and R and SAS and python have helped me be more efficient and a leader in the group.
My understanding of other math has come a long way since university with real tangible examples, I just don't learn from abstract examples I think.
I don't think there is a right answer here, I think there are enough opportunities in the field for there to be two branches of computer science much like there are two sides of business schools. The hardcore finance path and the businesss communication people tease so much.
i started a degree in cs last year and i noticed: most cs people i met @ my university have BIG problems with getting their hands dirty by writing some code. excellent in math, but not able to write some simple for-loops. finishing university without being able to code is kind of poor...
but i agree - abstraction is very important to handle complexity.
Guy who started out in math and ended up with a blended math and CS degree here. Their definitely were some parallels, but even in a program where they tried to combine the two, it didn't always mesh. I felt more like upper level math was a springboard for algorithms and proofs, so that by the time I got into algorithms I could say "hey I already have been doing combinatorics proofs very similar to this."
This should not be conflated with hacking though and software development. You can do both of those without any strong background in mathematics. Alot of software development and hacking is just using things people with CS skills built. I do not have to understand quicksort to use the native library sort.
Logic is important, but that is mostly orthogonal to any course of math instruction I've ever been exposed to. Now, if your goal is to be an academic computer scientist, then yeah, you do have to know some advanced math, just because the frontiers of research are so far out there. If you're aspiring to be a software engineer/developer, then chances are, outside of a few specialties, you may go weeks without using any math that an elementary-schooler couldn't perform.
A developer who is overly mathematically-inclined is actually a small red flag for me, because I have seen too many people who are very good at math and seem to understand theory, but absolutely cannot write code.
Anecdotally, I have seen much better results from developers who have experience in hands-on trades like mechanics, plumbing, carpentry, etc. There's an element of visualizing the entire system and how the components interact that seems to map over well to programming.
>Logic is important, but that is mostly orthogonal to any course of math instruction I've ever been exposed to.
I think that's the issue the author intends to address: computer scientists not having been exposed to a mathematical treatment of logic.
>you may go weeks without using any math that an elementary-schooler couldn't perform
If by "math" you mean "arithmetic and basic algebra," yes. If in "math" you include logic, induction, abstract algebra, graph theory, combinatorics, etc., then you're using math every time you write any code at all, whether it's ensuring you don't write any cyclic dependencies, making sure you correctly handle all return values, or making sure an algorithm doesn't require quadratic time or worse before you implement it. You need math if you want to know that your custom comparators won't cause a sort routine to blow up, to recognize certain possible sources of bugs in unsafe languages, and most of all to recognize and assess the validity of potential optimizations.
I mean, one can program with just a basic grasp of boolean logic and high-school algebra, but they'll have a hell of a time of it.
Naturally, it depends on the school, but the Software Engineering degree I have is heavy on Engineering, then progressing towards software. Lots of physics and math in the first two years, then two years mostly focused on modeling, covering compilers, relational algebra and similar core architectural subjects, progressing towards a final year focused on team management. Boundaries aren't as clear cut as I describe, but this is the progression. It is engineering, which happens to focus on software development.
I suppose that my negative reaction is mostly because, generally, calls for more math in learning a subject results in additional semesters of calculus or differential equations, which would be of doubtful use in my experience developing software.
> then you're using math every time you write any code at all, whether it's ensuring you don't write any cyclic dependencies, making sure you correctly handle all return values, or making sure an algorithm doesn't require quadratic time or worse before you implement it. You need math if you want to know that your custom comparators won't cause a sort routine to blow up, to recognize certain possible sources of bugs in unsafe languages, and most of all to recognize and assess the validity of potential optimizations.
For the most part, I view this as simple bookkeeping. Probably I've been doing this for too long and have just internalized it.
That is not the gist of the text. The text states that programming involves reasoning using abstract entities, and Mathematics, while not used directly in programming, is an excellent training ground for the ability to reason in an abstract realm.
I happen to agree a lot with the text. The best programmers, and most definitely the best system designers I know, excel at abstracting concepts.
But he doesn't really talk about how much math he thinks a CS course should have. I personally think a few weeks might be a good idea. Many months of math takes time away from other CS parts. He writes that maths helps you get better at abstract thinking, which I agree with, but he don't compare to other CS areas. For example, if studying compilers or data strcutures help you think abstract then we can perhaps have a compiler/data structure course instead of a math course. This would help students learn both abstract thinking and something directly CS related.
I think we can all agree that having only math for four years would make great abstract thinkers but probably very bad developers.
When I came to school I hated math, despite being relatively good at it. By now, my interests have changed and I'll graduate with a minor in math and a major in computer science, but largely in spite of my experience with collegiate mathematics classes rather than because of them. The way to get people to appreciate math isn't to force them to take classes on something they don't find interesting. It's to give them opportunities to see how it can be interesting or relevant to them and let them come to it when they're ready.
For myself I became more interested in math when I wanted to learn about graphics programming and machine learning. The first requires linear algebra and the second requires a lot of probability and statistics, which themselves require a solid understanding of calculus. By coming to mathematics this way I was able to see it's relevance to me and my interest in it grew the more I learned. Previously, when I was forced to take the standard multivariable calculus -> linear algebra sequence I did just enough to get by, since I really didn't care about the material. Once I had this small positive exposure to math that was useful to me it was much easier for me to appreciate more abstract math as well. But the important part is that no one made me learn this stuff! Had this been part of the curriculum I would have hated it, but since I was allowed to learn it when I was ready for it I found it very beneficial.