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I watched Gilbert Strang's video lectures on linear algebra (the MIT freshman course) for preparation for my PhD qualifier exam, and as a third year grad student, I could appreciate the relevance of almost every single topic in the class. That is, seven years after freshman linear algebra and with countless applications programmed, papers read and implemented, and theoretical/applied classes taken, it "all made sense" (don't ask me what a freshman is supposed to make of that material, other than to acquire it at a very abstract superficial level).

The early classes are the prerequisites for every and anything you might wind up doing with math. Including becoming a math prof, or a web dev, or dropping out. Nobody tells you, for every section of every textbook you have to read, what its myriad applications might be, and you can't get a customized build of just the topics you want.

But we're all startup people here right? Can this shortcoming be fixed? Can we make a detailed dependency graph of topics in applied mathematics, which could potentially be used to generate custom learning builds?