I have gotten through my first week of data science bootcamp at Metis and it has, so far, gone really well. It has been a lot of work, and we definitely have jumped right in, but overall I have not felt overwhelmed (yet). This week, we have covered basic pandas, python and git and focused on a group project involving NYC subway data.

Metis is composed mainly of a combination of lectures, pair programming, challenges, and projects. Though it has only been a week, I believe the projects and pair programming will be the most helpful. The pair programming is helpful because it gives us a chance to brush up on and refine our python skills, while getting experience with coding with others and learning their philosophies to programming. The projects are the most helpful because they will force you to learn and execute quickly in a short time frame (this structure is one of the reasons I opted for a bootcamp vs. learning data science by self study).

The two biggest things I am going to have to focus on are time management and organization. There are so many things I want to learn and dive into, but it’s not feasible for me to learn all those things in 12 weeks (if I ever want to sleep). I am going to need to realize that I only need to focus on the big picture—mainly, what is needed to get my projects done. This also applies to looking at data too. When looking at the MTA data,  we found many issues with it that were not immediately visible at first glance (like turnstiles that were reporting negative ridership). I want to be able to have the clearest, and most accurate data possible, but both in a bootcamp and business setting, that is just not feasible. I am going to need to sacrifice not having perfectly cleaned data in order to get projects done. 

As far as organization goes, I am just not that organized, but will need to be because of the velocity of files that are coming at me.  I can’t waste time searching for things. 

What I think makes this bootcamp such a worthwhile experience are the students in the bootcamp. Everyone is friendly, and willing to help out. Furthermore, we are all in the same boat—all studying and coding for long hours, (and are interested in the class). I think camaraderie is very underestimated attribute in a learning setting, and I feel this contributes highly to Metis’ success. Props to the interviewers who picked students from a wide range of experiences, but with similar personality traits conducive to learning well.

When I first started learning python, getting error messages left and right discouraged me, and made me question is I really wanted to learn this. Now, I expect errors are inevitable, and just a necessary step to getting the right answer. During this first week, I worked harder than I have had in a while…and liked it. In the weeks leading up to the bootcamp, I seriously questioned why I was doing data science in the first place (was it only for the money?). This first week has reminded me that I love to experiment, and try many different things out, and throughly analyze mine and other’s assumptions about the data and how things work—and these are why I wanted to do data science in the first place.