Whew. That was tough. Glad I don’t have to do that again. :)
But wow I learned so much! At the beginning of the week I was a python noob,but by the end I was looping through my dictionaries I had created and pulling out all the relevant data!
In this Metis program, we just dove RIGHT IN to working with data…and I mean RIGHT IN. The instructors gave us the link to the data sets, demoed a few quick useful ways to shape the data, and after that we were off! The data set was NYC MTA subway data which was gathered daily for every single turnstile in the city. It had attributes such as turnstile number, station number, date, number of entries, and exits. Our goal was to shape and clean the data and perform some exploratory data analysis.
I came into this very confident…it was just a csv file with 238k rows, how hard could it be?? WRONG. Very wrong. The challenges they asked us to complete were asking me to shape the data in ways that I was unfamiliar with. At first, I didn’t see the point, but by day 3 it was all starting to make sense to me, but that didn’t mean I had a perfect data set! Oh no, far from it. Still plenty of work to do. Although the more problems I came across, the more I learned. I had never felt so frustrated in something data related before. Get one thing working correctly, encounter another problem 5 minutes later…just like clockwork. However, my knowledge of python was growing and growing and by the end of the week I was infinitely better!!
Next, we were put into groups and our goal was to come up with a fictitious proposal from a company who wanted to glean insights from the subway data. When our group was brainstorming ideas, a health related proposal seemed most natural to us given that there is a high density of people in a subway station AND the vast network of lines allows users to get from one end of the city to another very easily and quickly; a definite positive for infectious disease spread. We decided to have a bit of fun with this and do some exploratory data analysis for the government to let them know how to properly station their police forces to prevent a zombie outbreak. Although it seems silly at first, this type of situation is analogous to any major disease outbreak.
Day 5 came along and my team presented our findings to the class. We recommended that forces be staffed more heavily during morning commute times and on week days because that is when subway ridership was the most prevalent. We also proposed the product we would like to build our client given they accepted our proposal.
All in all, it was a whirlwind week of frustration, triumph, and most importantly, meeting some brilliant people who I can’t wait to learn from and gain knowledge from in the coming weeks! So far I am absolutely loving this bootcamp environment and I can’t wait to see what my cohort and I accomplish in the coming weeks!
Until next week.
~ Trevor