This was the first week of the Fall 2016 Metis Data Science bootcamp; from now on, I will be living and breathing more data science than ever before.

My name is Laura Colón-Meléndez. I recently finished a PhD in Physics and have spent the summer teaching myself Python, going over statistics, and learning a bit about machine learning.

The bootcamp began last Monday, September 19. There are 27 students in the cohort, which will graduate in mid-December. This week, we went over Python dictionaries, code execution efficiency, comprehensions, some matplotlib and seaborn, and pandas (which we will go over in more detail this upcoming week). Weekdays at Metis begin with peer programming – a really nice activity, I would say, as you get to learn how other people think when they program. As in physics, in Python there are many different ways to solve the same problem. This week, our instructors chose peer programming problems to solve that would teach us about algorithmic complexity and how to write efficient code.

We dedicated this past week’s afternoons (and evenings) to work on our first project, Project Benson. On the first day of the bootcamp, we were assigned to groups, and given the task to get actionable insights from MTA subway turnstile data. We had to do a bunch of exploratory data analysis and deep-dive into pandas. On Friday afternoon, each group (there were seven) presented their findings to the cohort. We’re an interesting and smart bunch of people!

I have a feeling that the next 11 weeks will go by very fast, and that there will be a lot of work to do. Thankfully, there is free beer at WeWork NoMad, where Metis is located in NYC. I’m happy that I have to walk there every morning; I am not sure how else I will be able to exercise while this bootcamp is going on!