Data Science as a Genre
An article I read in college titled, “Big Picture People Rarely Become Historians” (Russell, David R., and Arturo Yañez) outlined how in writing, the norms and conventions you have been conditioned to affect the way you approach problems. For example, if you are an award-winning fiction writer, there is no guarantee that your skills will transition you to a career in journalism until you accumulate the necessary perspective.
Never in my life did I picture myself wanting to learn data science. Not because I had a particular disdain for the field, but because it never really appeared on my radar.
It was once I began studying Government and Politics (GVPT) at the University of Maryland, that I registered the field. For those in the major, one political “SCIENCE” course was mandatory. The course centered around using STATA for political science. I look back at that semester now and cannot help but think how easy they went on us… We didn’t scratch the surface. We were just on our way to said surface.
Needless to say, the course’s mandatory inclusion did not garner much praise from students. I can admit I did not take it very seriously as a result. But if I could go back in time I would not change that.
In school, I was knee-deep in political theory. It fascinated me and simultaneously got my blood boiling. I was no expert of course, but I was starting to get a solid foundational understanding of how I should navigate the field.
This feeling of progress and was further bolstered by my experiences working at various think tanks which focused on governance. It was here that I got to see, in person, experts in the field analyzing issues right in front of me with great strength and intelligence. I was given tasks that required my own analysis and was able to spend a great deal of time and effort strengthening my critical reading and writing skills, as it related to political “science”.
And most recently, as an English teacher in Japan, I was able to step outside my comfort zone and immerse myself in an environment that was completely foreign to me and a position I never expected myself to be in. This was without a doubt the most difficult role I have had. I was never a particularly talented public speaker, nor did I go to school to be a teacher. At the start, each day was a struggle. But I kept at it. I’d work, prep, then sleep. Except for Saturday nights…Shibuya called for me.
Around 5–6 months in, however, something clicked. No longer did I stress about teaching particulars. I could focus on students, multitask, and be more flexible in the way I taught. The question for me every day was not, “how can I avoid failure?” but “how can I become better?”.
Going from political science to teaching was somewhat jarring however. The differences were stark and required that I did not approach the job as a political scientist. I had to refocus my perspective. Resisting (and noticing) these conditioned reactions was key to speeding up the process in which I became far less frustrated with the workings of teaching.
Now when it comes to data science I am in the thick of it. My previous experiences do come in handy sometimes, but before I can start mixing and matching, I must go through that process again of making myself into a blank page and reflecting on my successes and failures. Once I get a grip on data science, then I can use my previous skills to serve as a multiplier effect.
