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Olga Khazan writes in The Atlantic that learning to program involves a lot of Googling, logic, and trial-and-error—but almost nothing beyond fourth-grade arithmetic.
Victoria Fine explains how she taught herself how to code despite hating math. Her secret? Lots and lots of Googling. "Like any good Google query, a successful answer depended on asking the right question. “How do I make a website red” was not nearly as successful a question as “CSS color values HEX red” combined with “CSS background color.” I spent a lot of time learning to Google like a pro. I carefully learned the vocabulary of HTML so I knew what I was talking about when I asked the Internet for answers."
According to Khazan while it’s true that some types of code look a little like equations, you don’t really have to solve them, just know where they go and what they do. "In most cases you can see that the hard maths (the physical and geometry) is either done by a computer or has been done by someone else. While the calculations do happen and are essential to the successful running of the program, the programmer does not need to know how they are done."
Khazan says that in order to figure out what your program should say, you’re going to need some basic logic skills and you’ll need to be skilled at copying and pasting things from online repositories and tweaking them slightly. "But humanities majors, fresh off writing reams of term papers, are probably more talented at that than math majors are."
Original Submission
(Score: 2, Informative) by brocksampson on Friday September 04 2015, @04:58AM
I am a physical scientist and an experimentalist and I end up doing quite a bit of coding to interpret data and to see how those data comport with theory. That programming involves transforming huge matrices and sets of numbers where small mistakes with precision, sign, type, etc. can ruin months of work. But I am absolutely terrible at math(s), so I rely on theorists (who in turn rely on real programmers) to write libraries and such that I can then apply to complex experimental problems. But they are not programmers either and do not write shiny UIs with help balloons (or documentation), which means that I end up doing a substantial amount of coding and scripting to generate fancy plots from my data. The beauty of modern computing is that I can know exactly how a series of experiments led to a particular plot and at the same time a theorist can know exactly what my data must have looked like to get their equations to make said plot. That back and forth is essentially how scientists talk to each other in interdisciplinary fields where no one can be an expert at everything. My expertise is complex experiments that are incomprehensible to theorists whose expertise is mathematics that are incomprehensible to me. Yet we often meet, intellectually, over a piping hot git repository of Fortran. (Of course, none of are stupid, either; we all deeply understand the scientific questions at hand, just in different ways.)