Some Observations on a New Possible ‘Calling’

June 8, 2008

This weekend has been abnormally productive. Okay, probably not productive in the normal sense of the word. But still. I got a lot of [important] thinking done.

As I’ve mentioned before, computational chemistry seemed like a real option for me, something that doesn’t exactly get much pull in a high school career choosing system. At the time of that post, though, I just thought this would be a one of thing, something that’s pretty rare and not at all a sure way for me to go.

Then I started this weekend looking for information on computational chemistry, with an emphasis on Python (apparently one of the de facto languages for programming computational chemistry-type programs). That lead me down a long and windy road to the whole field of ‘computational science.’ Which, when I first read the name, sounded a lot like ‘computer science.’ I mean, they both have the ‘comput-‘ in them. But they turn out to be quite different things.

The basic difference? Computer scientists are worried about the basic structure of the computer, like the architecture and information processing. Computational scientists, on the other hand, use computers to solve real world scientific problems, mainly through some sort of mathematical modeling. While I have nothing against computers and computer science (and obviously they form the foundation upon which computational science stands), I personally find the computational side of the street much more interesting.

With that in mind, I’ve been devouring books on computational science, focusing a lot on Python and Maple (a computer algebra system [CAS]: think your graphing calculator, on steroids). Computational science is basically a subfield of applied mathematics. Unfortunately, Ursinus has nothing even close to an applied math curriculum (we have all the basics, like linear algebra, diff eq and numerical analysis, but nothing really far beyond that, and even those classes focus a lot more on the theory than the practice). So I’m kind of on my own learning the material that will be necessary to succeed in grad school. Which I’m cool with, because it means I can teach myself something that I’m not going to have to ‘learn’ again in some future class.

Like I’ve said before, I kind of wish I’d had this insight before. Like, maybe two years before. Could have saved me a whole lot of trouble with this whole chemistry thing (though I’m glad I’ll have a major in a laboratory science, because then at the very least I can emphasize with other lab scientists in the future… plus the skills never hurt), gone to a school and majored in either engineering or applied math, and circumvented this whole convoluted path I’ve taken to get to where I am today. But it’s a little to late for that now. So I’ll just work with what I’ve got and go from here. I figure I’ll do the chemistry major, definitely pick up the math major (man, I’ve oscillated between on and off with that bugger for some time now!), and try and pick up as many computer science / physics courses as I can along the way. At least I won’t have to think much more about my schedule from here on out: it’s basically planned as is, with every available slot filled.

This turned out to be a lot of me talking about myself. Sorry, gang, but I just felt like this was a major breakthrough for me, especially because I can really see myself being a computational scientist and LOVING it every day. Something I couldn’t honestly say about being a lab chemist. Plus, it combines my best skills with my scientific interests [matrices, vectors, differential equations, Fourier and Taylor series, what more could you ask for?]. It’s a win-win situation.

Time to put my head to the grindstone and make up for some lost time.

Namaste.

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