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’m a mathematician by training who has worked extensively (and exclusively) in the software field. While I realize I’m probably biased here, I think I write very solid code and have rarely received any complaints from trained software engineers about it.
I did however also take quite a few computer science classes in college and have spent a lot of time learning how to write better, more readable and maintainable code. Having had quite a few jobs at the start of my career where I was the only programmer on a project and therefore forced to eat my own dog food has certainly also helped.
Interesting. Im curious, what are some key areas of math that you think is the most interesting/useful for software engineering (that you would personally recommend learning)?
I will likely have some spare time in the following months and i currently plan to spend it on deepening my senses related to linear algebra and analysis.
I majored in math and have so far a great career in software. I don’t think knowing math separates me out from CS grads generally. However, math majors largely chose to major in Math because we like problem solving. Plenty of CS grads major in CS because they are expected to. Being a passionate problem solver gets you pretty far.
Yeah, I think you’re already on the right path with that, those are good basics for anything computer science related (and usually required classes if you take CS in college). Perhaps add Numerical Analysis to that list.
Also, Operations Research has some interesting optimization algorithms, and Statistics is useful for anything related to Machine Learning.