341–350 of 465 posts
Good code doesn’t just solve a problem, it solves it in a way that’s readable and modular. I think the problem-solving part of coding requires math skills, while the organization part requires writing skills. The organization part affects the problem-solving part, because if you write messy code (that you can’t reread once you forget or extend without rewriting) you’ll quickly get overwhelmed. Writing large math proo…
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I don't think that quote really supports coding and math being equivalent. To me, the quote provides an abstraction of math through a structuralist perspective. Language can also be viewed through this abstraction. I think coding could share the abstraction, but that doesn't make the three of these fields equivalent.
> coding and math being equivalent Please see lambda calculus. I mean equivalent in the way mathematicians do: that we can uniquely map everything from one set to another
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It's definitely a gray area. Is a DAG traversal algo "math", or is it more computer-sciencey? What if you do it in SQL? Certainly there's a mix of more or less concentrated logic/math vs glue code, and most of that is very dependent on the domain you're working in. I find this distinction useful in the abstract, that one can engage different parts of the brain for different components of development. This probably ex…
> Is a DAG traversal algo "math", or is it more computer-sciencey? Firstly, computer science is math. Secondly, I remember covering graphs in a discrete math course back when I was in college. > What if you do it in SQL? SQL is more-or-less a poor implementation of relational algebra. Ie, math.
But it's hardly a useful grouping any more. You can study and do well in computer science with minimal knowledge of most of the core mathematical subjects.
While graph theory certainly crosses over into math, you can cover most of the parts of it relevant to most computer science as a discussion of algorithms that would not be the natural way of dealing with them for most mathematicians.
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Most coding doesn't need much of any math past boolean logic and very basic set operations. I'm much more likely to spend my time studying DB and interface schemas to understand how something works than doing a lot of mathy fiddling. Sure, some people write game engines and such, but even much of 3D graphics doesn't need anything more complicated than the first half of a linear algebra course.
> Most coding doesn't need much of any math past boolean logic and very basic set operations Coding IS math. Not "coding uses math", I mean it is math. Mathematicians do not deal in objects, but in relations among objects; they are free to replace some object by others so long as the relations remain unchanged. Content to them is irrelevant; they are interested in form only. - Poincare[0] I don't know how you code, b…
In other words: It's math in a sense that for most of us with a computer science background is often not very relevant to how we work.
Good code doesn’t just solve a problem, it solves it in a way that’s readable and modular. I think the problem-solving part of coding requires math skills, while the organization part requires writing skills. The organization part affects the problem-solving part, because if you write messy code (that you can’t reread once you forget or extend without rewriting) you’ll quickly get overwhelmed. Writing large math proo…
> Good code doesn’t just solve a problem, it solves it in a way that’s readable and modular. Strongly disagree. There are plenty of cases where non-modular code is more readable and faster than modular code (littered, presumably, with invocations of modular logic). There are also countless cases, particularly in low-level languages or languages with manual memory management, where the best solution -- or the correct…
I'd say that readability, which often boils down to consistency, and modularity are ways to do this, but they aren't the only ways. And as you say, sometimes there's a need for "unreadable" code, so not everything can be easy.
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Early, strong reader here. Off-the-charts spatial reasoning, as measured by tests. Terrible at math, I hate it and feel dyslexic trying to read most mathematical writing. I excelled at it in elementary school, then quickly came to feel frustratingly stupid at it as it became less about algorithms (more on that in a bit...) and all about equations and abstract stuff with unknown applications. However, programming was…
Same but I am literally math dyslexic (formal diagnosis of dyscalculia). It prevented me from having a CS degree, I was unable to complete the math courses, but as far as actual programming and "software engineering" goes (design, etc) it's never hindered me. I can work out the logic and I let the computer do the math. Edit: I'm downvoted below zero for this comment. I don't know what people are so offended by?
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> Most coding doesn't need much of any math past boolean logic and very basic set operations Coding IS math. Not "coding uses math", I mean it is math. Mathematicians do not deal in objects, but in relations among objects; they are free to replace some object by others so long as the relations remain unchanged. Content to them is irrelevant; they are interested in form only. - Poincare[0] I don't know how you code, b…
>Coding IS math. No, not always. Quite a lot of high-level code doesn't require any math at all. It doesn't take math to perform CRUD operations, which account for a lot of programming work. Sure, the underlying machine code is all based on math, but the higher level programming doesn't need to involve a single math equation for it to be useful. Let's see where the goalposts move now...
Though middle-school or not, it's still math.
As others have commented, I believe strongly that to define “language brain” and “math brain” is a false dichotomy and bunk neuroscience. Mathematics IS a language in itself; a collection of symbols created to express concepts. This study works really hard to make a statement that most people already know: anyone can be a good programmer, mathematician, “whatever” if they have the interest, drive, and opportunity to…
To begin with, the study measures functional numeracy: the ability to solve everyday numerical problems. This is quite different from the kind of advanced mathematics often associated with programming, such as formal logic, symbolic abstraction, or the use of formal languages (as found in denotational semantics or type theory).
These more abstract skills—not basic arithmetic—are essential for understanding recursion, type inference, or algorithm design. That functional numeracy has low predictive power in this study does not imply that deep mathematical reasoning is irrelevant to programming.
Moreover, the language used in the study is Python, which was explicitly designed to be readable and semantically close to natural language. This may give an advantage to individuals with strong verbal skills, but the results don’t necessarily generalize to languages like C, Lisp, or Haskell, where symbolic and logical density is much higher.
Finally, language and mathematics are not opposing domains. They share cognitive underpinnings, such as working memory, executive attention, and hierarchical structure processing. The key is not which one "wins," but how they interact and complement each other in different programming contexts.