My Experience with Flatiron School — Part 2
In my previous post, I went over my career change, and how I went about choosing Flatiron, as well as the interview process. In this post, I’ll be going over my first few weeks at Flatiron, as well as some of the things we covered. You should know, that the curriculum at Flatiron changes on a regular basis as they collect feedback from students as well as what the school is seeing in the job market.
Week 1
First day of Flatiron, we’re all ushered into their building down in Bowling Green. There are some 60 odd people joining me, all new students for Flatiron’s 3 primary boot camps — Data Science, Software Engineer, and UX/UI design. We’re all given name tags and told to sit anywhere. I take the opportunity to get to know people, and am pleased to find 2 of my table mates are in the Data Science course. Both of them happen to have Master’s in Public Health. Not having a hard science background, i’m nervous. We discuss our progress on the pre-work, and i’m relieved to discover we all had difficulties with the Python. After the standard “Welcome to…” spiel, we are divided up by class and sent off to our respective areas. The Data Science class is located on the other end of campus, now known as Data Science Space Camp since its so far away from the rest of the campus (really just a long hallway away). There we are assigned seats among groups of tables and told to get set up. We then play a few ice breaker games where we try to get to know everyone.
My class was oversized. We had 23 people, and they had a range of backgrounds and specialties. We had 6 college grads, who all majored in hard sciences like math, computer science and actuarial science. We had 4 finance people, 3 who worked as analysts, and 1 who worked in financial technologies and trading systems. We had 2 people who had worked in the healthcare field, who both had Master’s in Public Health. We also had two engineers, a dentist, a teacher, and a consultant. This isn’t everyone, but we were all generally dissatisfied with our careers, and our technical backgrounds varied from expert, to neophyte.
The first week is hard. The day breaks down as follows. Show up at 9 AM, practice pair coding with Python for 1/2 an hour. We are given coding challenges from various websites like hackerrank, codewars, leetcode and various other sites with python code challenges. After that, we are given 2 hours to study lessons on Learn.co, Flatiron’s online curriculum. These lessons start off basic, teaching us things like lists, dictionaries, lists of dictionaries, for loops, while loops, etc, and how to interact with them. At 11 am, we start our first lectures for the day, where we go over the things discussed in the labs on Learn.co, and practice more complicated versions of the lessons, or learn to integrate them in new ways. This is followed by 1 hour for lunch.
When we get back from lunch, its back to studying Learn labs, and then dive into the next lecture at 2 PM, where we discuss other labs. The pace is blistering at Flatiron, you’re expected to go through 10–20 labs per lecture, although its not required. But if you want to keep your head above water, you’d better keep your nose to the grindstone. After lecture its more studying until 5 pm, then you get to go home.
By Wednesday we were given our first project, called Rolling Stones. In it, we are paired with a partner, given a CSV file of top albums, and tasked first to convert it into a list of dictionaries. Then we are to use our python lessons to tease apart this dictionary and answer a list of questions.
Things like:
Build a function to find an album by year, by name, or by a set of years.
Build a function that returns a list of artist names, or album names
Create a histogram of genres and years of all albums in the list.
My experience with this project. Utterly demoralized. I hadn’t kept up with the lesson plans, and python confused me. Fortunately, I the student I paired up with had a Master’s in Probability, and was already familiar with coding. With his help, I was able to understand the underlying structure of the listed dictionary, and use some list comprehension to tease out the necessary functions. Having said that though, I still really struggled with the python and finished out the week insanely tired from stress and demoralized with how bad I was at learning and understanding python.
I was not alone in this, and by the end of the week, we had our first drop out, one of our MPH students who struggled as much as I did with the python and determined that this course of study was not for him.
At the end of the week, we have what’s called Feelings Friday, where we get together in a circle and share with the group how we felt about the week. Some of us were pretty down, some of us were excited and looking forward to what were going to learn the next week.
All in all, I have to say, the first week was the worst part of the program for me. I hadn’t done enough of the pre-work to be comfortable with Python, and boy was there a lot of python. Loops were absolutely foreign to me, and I found I couldn’t grasp how the code was structured and in what order to place things. I found myself going to sleep at night, exhausted, and thinking how great it would be if I didn’t wake up the next morning. I wasn’t suicidal, but man, I certainly was defeated. Fortunately, if you’ve seen my other posts, it obviously gets better and more exciting from here, but I wanted to let you dear readers know how I felt, and know that its tough, but you’ll overcome it.
That’s all I have to say for this week, tune in next week for my turnaround!
Follow along for Part 3!
