It is often echoed that mathematicians make excellent software engineers, and that their logic-adjacent work will translate efficiently into coding and designing.
I have found this to be almost universally untrue. I might even say the inverse is true.
While I and many of my peers have capacity to navigate the mathematical world, it certainly is not what sets us (at least me) apart when designing clever algorithms and software tricks.
Point being: I dont think the property/trait that makes good programmers is mathematical literacy.
I would love to hear what others experience is regarding this.
I think the same can be said for a lot of fields. E.g., just because someone’s an excellent architect doesn’t make them a good animator by default.
There’s also so many variations on the types of programming. Maybe a mathematician might be better suited for data science rather than frontend stuff. And even then, each person is different and has their own set of skills part from whatever their formal training is.
What I think makes good programmers is having the ability to bash your head against your desk while debugging, but still walking away at the end of the day loving the job and problem solving. Persistence and creativity go a long way in programming.
What I think makes good programmers is having the ability to bash your head against your desk while debugging, but still walking away at the end of the day loving the job and problem solving.
Just quoting you for emphasis here, in case any of our newbies missed it. Well said!
I’ve known a lot of math people, and /on average/ I think they’re more capable of programming useful code than the other college graduate groups I’ve spent a lot of time working with (psychology, economics, physics) /on average/.
That said, the best mathematicians I’ve known were mostly rubbish at real programming, and the best programmers I’ve known have come out of computer engineering or computer science.
If you need a correct, but otherwise useless implementation, a mathematician is a pretty good bet. If you need performance, readability, documentation, I’d look elsewhere most of the time.
Mathematicians are good at writing algorithms, but not at the development aspect, which is basically building for different systems, packaging software and documentation.
I would disagree on the performance part, the vast majority of software developers aren’t writing high performance software and the ones that are tend to be computational mathematicians or physicists.
Just because you’re not writing high performance software doesn’t mean it shouldn’t be a consideration. Sure, I’m not gonna micro-optimize memory when I’m writing an API in Python, but that doesn’t mean I’m not going to write it efficiently.
If I have to store and then do lookups on some structured data I’m gonna use a hash table to store it instead of an array. If I need to contact a DB multiple times I’m only gonna close my connection after the last query. None of this is particularly difficult, but knowing when to use certain DSA principles efficiently falls pretty firmly into the computer science realm.
If you need someone to hyper-optimize some computations then a mathematician might be a better bet, but even those problems are rarely mathematician level difficult. Generally software engineers have taken multivariate calculus/differential equations/linear algebra, so we’re decently well versed in math. Doesn’t mean we don’t hate the one time a year we have to pull out some gradients or matrices though.
In my (anecdotal) experience, software is best approached as an engineering pursuit. Almost anyone can write code, but building code that is maintainable, scalable, and reliable has a lot in common with building other things that we want to see similar features in.
There are plenty of math people who are good at this, but there are lots of other fields of study that are just as adjacent (philosophy and communication both come to mind).
I would say, having an affinity for math can be a sign of affinity for programming.
Logical abstract thinking, order of operations, and problem solving all transfer. A for loop is just a summation equation for example.
But id say in today’s high level development, being math centric is less important.
To be good at programming, a lot of knowledge is needed, but “accidental”. From practical ones like how to use git, to conceptual ones like cache performance mental model. It’s perfectly possible that git is designed with a different CLI, or the common cache line size being 512 bytes. Mathematicians usually don’t care about these things, since they are accidental. So they are bad at writing programs that’s far away from math.
It’s a completely different story when they are writing programs about math. If the tool is good enough, i.e. allowing them to express math ideas in familiar terms, mathematicians are very good at writing math programs. As can be observed in Lean and mathlib.
