The first week of the boot camp is in the books, and it’s off to a scorching start. While the pacing was somewhat of an adjustment to get used to, after finishing a years-worth of intense study for my master’s degree it felt a little like riding a bike. The first day was complete with introductions, administrative duties, and even a preliminary lecture on Pandas. While the lecture was effective and broad, it was different than a traditional academic course. Specifically, I really enjoyed how applied and practical the instruction was, and this aspect of the boot camp is exactly what I signed up for.

At the end of the day we were divided into groups of four and were given our first project: Project Benson. The premise was more of a lack of premise. We were given turnstile data from the New York City Metropolitan Transport Authority and were asked to act as a data science consultancy. Our fictional client needs help optimizing some feature of their business using this data, and we need to come up with a recommendation for them through a detailed exploratory data analysis. The point of the project was not only to give us practice exploring and visualizing data, but also to allow us to express some creativity while gaining valuable group work experience.

After throwing a few ideas around, we decided that our fictitious client would be called BSKR (vowels purposely excluded). The company helps professional buskers (street performers) maximize their earning potential through a scheduling platform while increasing viewership for their network as a whole. Essentially, we would find the busiest stations in terms of foot traffic, and would allocate BSKR’s clients equitably amongst these destinations. Therefore, performers are placed in strategic areas to maximize their potential earnings, but aren’t stepping on each other’s toes in the process. Presentations for the project were held on Friday, so we had little time to waste.

After Monday, the schedule for the next three days was more or less as follows:

• Pair programming challenges for about an hour to start the day.
• Lecture until lunch.
• An hour and a half break for lunch (definitely a nice time to catch up on work, but also to socialize and get to know one another).
• After lunch, there would usually be some additional supplementary lectures for an hour or two.
• The last bit of the day was reserved for working on Benson.

Once Friday rolled around we still had a pair programming challenge, but were given the rest of the day to work on the finishing touches for our presentations. After lunch we presented to the cohort, and were asked questions and given feedback (both positive and negative) on our project as a whole. After writing up our final proposal, we submitted all of our materials to GitHub and called it a week. Benson is officially in the books as the first entry into our Metis portfolio of projects, with four more to come throughout the next 11 weeks.

Some general thoughts on week 1:

• Fast paced, but effective nonetheless.
• Loving the applied nature of the program thus far. We ended up really getting our hands dirty with data munging and exploring a fairly messsy data set, which is great experience.
• Experience presenting is invaluable. A good data science project means little to nothing if you don’t know how to effectively communicate your findings!
• The cohort is from a diverse set of backgrounds, including Physics, Economics, Biology and Health Care, among others. It’s great getting to know so many interesting people with such varied perspectives.
• The food in SF is great! The coffee, too.
• I’m excited for basketball season to start up. The group has quite a few NBA fans, so the communal fandom and banter should be fun.

While the short deadline for Benson and amount of material made the first week a bit daunting, I have genuinely positive reviews of the experience thus far and am looking forward to what the next 11 weeks has in store.