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That's nice. The entire field of machine learning is pretty much entirely applied calculus. Integer and real calcului are also rather important when it comes to solving constraint-satisfaction and constraint-optimization problems. Really abstruse, ivory-tower stuff with no real application to the real world. Like job scheduling. Oh, wait.... Many code optimization problems are NP-hard, like register sufficiency, code generation with unlimited registers, program equivalence or inequivalence, etfc... these kinds of problems are often best solved with constraint-optimization techniques. Is writing an optimizing compiler too theoretical and academic for you?
Why? Update: coreolyn, I didn't mean to imply that math was the only solution to the problem of teaching people to think analytically and abstractly, although I believe it's a good solution. The point is, though, that "good" software engineering practices are useless unless you know when and how and why to apply them, and you can't learn that merely by learning the syntax and semantics of a programming language. It's like knowing how to play an instrument, but not how to play in a given key or put together a chord progression. -- In reply to Re(2): (OT) Should math (or adv. math) be required in CIS degrees?
by FoxtrotUniform
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