1–10 of 165 posts
(Please remember that voting does not up vote the poll.)
- Want to even think of committing to scalaz/haskell? Category theory
and lambda calculus.
- Want to develop a physics engine for a modern 3d shooter?
Geometry, trig, single/mutli-variable calc, algebra, and differential equations.
- Want to do some NLP? Discrete math, automata theory,
not to mention a strong background in linguistics.
- Want to do some ML? Bayes statistics.
- And so on..
Edit: This was prior to OP clarification. :)
Programming can be a mix of many things: reasoning about associations and state, designing processes or interfaces, or—most saliently—modeling a domain that may or may not mathematical in nature.
To compare a programming language/runtime with a US math curriculum makes about as much sense to me as asking for the Richter magnitude of a sledgehammer.
That said, I spend 90% of my time writing geometric analysis libraries, so I am biased.
Also, memory, which is all we are manipulating at the end of the day, is linear; points on a line.
However, concepts such as recursion, and other metaprogramming don't really make sense at that level.
You can make rote crud apps with an algebra level understanding of programming.
I have always found geometry to be the most similar in terms of content. Geometry is where most people are introduced to proofs, which are similar in a lot of ways to programming. Also, you often need to know some amount of geometry in even CRUD apps to properly deal with interfaces, charts, etc. That said, I spend 90% of my time writing geometric analysis libraries, so I am biased.
Some programming problems require specific areas of mathematics, some others, some none at all. All programming, as well as all real mathematics, require the ability to reason logically about a problem.
Based upon this understanding, I can't chose any of the poll options, but if I had to, I'd go with geometric proofs as well.