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Mathematicians and software (lemmy.world)

submitted 10 months ago by 3h5Hne7t1K@lemmy.world to c/programming@programming.dev

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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.

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[–] CmdrKeen@lemmy.today 4 points 10 months ago (3 children)

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.

[–] 3h5Hne7t1K@lemmy.world 1 points 10 months ago (2 children)

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.

[–] CmdrKeen@lemmy.today 1 points 10 months ago

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.

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