Well it’s been quite a while since I posted something, the reasons for which will constitute another post soon. Basically, the better part of the past year involved struggling through the uncertain, difficult precarity that is the academic job market while finishing the bulk of my dissertation. Anyone interested in the music theory job market in general should check out Prof. Megan Lavengood’s excellent post on her own experience with some nice overall stats, and Kris Shaffer’s post from a couple years ago. My experience in short: I was lucky, thankful, and extremely relieved to receive a 2-year visiting position after a grueling process that lasted from August until late April. I worry the market will be even worse during the next go around. Such is life in the academic rat race.
This post, though, is a brief summary of a side project I undertook smack in the middle of the job market process back in February. This exercise was basically a way for me to learn some new Python data management and graphing stuff, and was initially meant as a fun thing to do (i.e., procrastinate) to practice some programming. Essentially, I wanted to build a database of all songs to appear on the weekly Billboard Hot-100 charts, which I could then use to search for correlations or trends between genre tags, intertextual connections, or acoustic phenomena. I didn’t do much besides make some figures that track trends, but I’m hoping this can serve as the basis for a larger project. Continue reading