I'm a Data Scientist Now, Right?
My first week at Metis is complete, and, to be honest, it went much smoother than I expected! I think doing all of the pre-work helped, but I also was able to draw from some of my prior experience and knowledge from MOOCs, reading, some small side-projects, work (Epic, largely), and wonderful instructors.
So far, I’m really enjoying the bootcamp model. The lectures are short and sweet, but thoughtful, intentional, and well curated. I find the latter to be particularly important, as there are usually multiple ways to do something in Python and simple tasks can feel complex. As a result, I don’t feel too overwhelmed and when rereading the lectures, I typically pick up on something new.
I also like the project-based approach. With my wonderful Chicago peers Max, Mitch, and Anna, we performed exploratory data analysis on MTA Subway data to optimize a team of canvassers collecting signatures for a hypothetical Women in Tech organization. The project taught me that: the subway in New York is complex and that key insights and recommendations do not necessarily have to come from convoluted, black-box-y technical models.
Outside of bootcamp, but data related, I’ve been following COVID-19. I’m not reading the news as much as I usually do, as bootcamp is keeping me busy and reading the news right now can make me feel a bit helpless. However, I’ve particularly enjoyed the following websites, which provide some key metrics to keep me in the loop:
The projections are quite interesting, especially when performed on a state level. The NYT’s Daily podcast had a nice overview on the shortage of hospital resources (e.g. beds, ventilators, N95 face masks) that complements the data and explains the policies (some with roots in the 1970s) behind some of these shortages. The latter website is simple, but has a nice, interactive UI and gets me some quick Chicago data.
Stay safe everyone! Our bootcamp is online for the foreseeable future, so we’ll be doing our part!