In the most wonderful ways, Metis has taught me how endless the humbling can become. It’s been a truly enjoyable experience to be brought back to a beginner’s mindset.
This was the week of linearity: linear algebra, linear regressions. We routinely covered semesters of material in hours. Add web scraping (feat.
selenium), learning the
pandas, starting in on a new data science project, and yet more statistics (hypothesis testing and intros to Python’s
sklearn), and you have a whirlwind of a week.
It’s been mind-expanding; it’s been fun. It’s been how I like to get exposed to things: all at once.
Oh, yeah...I also gave a half-hour presentation on Markov chains and formal language. The talk included a demo of my very own (and not particularly slick--though I’ll probably post it to Github anyhow) homemade Markov text generator, couched in a series of analytic-philosophical contrasts drawn between formal and natural language. Markov chains exemplified the depth and the power of processes unique to formal language, I argued.
Particularly at Metis where all is set up in so beautifully intentional a manner, every experience is, in a big way, a learning experience. My well-too-long philosophical preamble to Markov was an exercise in how not to give a presentation. Main advice to self: don’t read off slides. Everyone could see what was on the slides; they could read. I’ll aim in future presentations to say things that work in contradistinction to my slides. Just like Colbert back in the good ol’ days of the Report.
After this fiercely challenging, incredibly rewarding data science bootcamp week two, amidst all manners of new material, I recognize a few things I do consistently well. I think critically, write compellingly, and play nice. I’m the easygoing, ever-positive, conflict-diffusing, calm-under-pressure teammember. We might discuss a problem over tea. I can be that guy.
I couldn’t help but feel like a bit of a burden to those classmates who have more familiarity with the material than I do, as I drew them out in unpacking their ideas and strategies over the week. So I was always a bit surprised to find these classmates saying that they actually enjoyed the process of explaining their thoughts to me.
I was all the more surprised when one of these far-more-savvy-than-I classmates observed in the middle of a conversation at the end of the week, quite bluntly, “Everyone likes working with you.”
I explained the kinds of things that I’d been working on with others: not only particular procedures and syntactic nuances of Python and BeautifulSoup, but also best practices for writing maintainable code, for testing and for debugging, for doing data analysis on matrices, for graphing, for web scraping...
“So you were learning how to think?”
“Yeah!” I jumped at the thought. From what I’ve thus far gathered, data science is more about practices of thinking and a willingness to learn new things than it is about regurgitating what you already know. Learning how to think--exactly: this, to me, was learning.
“Oh,” my classmate said. “I already know how to do that.”
At least I have my openness. At least I have my optimism.
(The epigraphs are from, respectively: T.S. Eliot, Four Quartets; Eugene Ionesco, Notes and Counternotes; and Sasha Laundy, Your Brain’s API: Giving and Getting Technical Help.)