This is the first post in a series on my experiences attending the Metis immersive Data Science course in New York City. My subsequent posts cover pre-bootcamp and Week 1 and Weeks 2-12. While I cannot guarantee anyone else’s bootcamp will be the same as mine, I hope you find these posts as useful as I found other blogs when I was researching data science programs.
I distinctly remember that filling out my first bootcamp application was the moment in my journey towards data science that I thought, this is really happening. Although I had been planning to leave my job for a little while and had been learning some data science fundamentals through MOOCs beforehand, applying felt like my first serious step in changing careers. In determining to which immersive programs I wanted to apply, I found the personal testimonials of blogs to be among the most helpful resources, more candid than program sales pitches and far more in-depth than most reviews on SwitchUp and Course Report. Given what a gigantic leap applying felt like to me I am a bit surprised I did not find more posts about the admissions process, and that is what motivated me to write about my experience.
For the sake of brevity I won’t delve much into the details of test questions, interviews, or programming challenges, especially since most of that information is readily available online. If a particular data science program doesn’t describe their admissions process on their website you should be able to get a full rundown of entrance requirements by emailing them. I will also add the caveat that my admissions experiences are limited to just two in-person, immersive programs in New York City, Galvanize and Metis, which represent only a fraction of the full breadth of data science classes. I won’t get into why I picked Metis here, but will likely write about my decision in a future post. While I researched as many options as I could find, I eventually settled on applying to two schools, a limit I recommend not exceeding if you will be applying simultaneously and are working full-time. Data science bootcamps generally have a limited number of spots per session and use the admissions process to find the most suitable candidates. It’s important to know that programs vary significantly in the difficulty and length of their admissions processes; some programs only require a couple of online interviews while others, like Galvanize and Metis, employ far more extensive screening.
Initial applications tend to be fairly uniform, consisting of an online form asking who you are, where you’re from, why you want to study data science, and when you want to start, all (hopefully) easy questions that shouldn’t require more than an hour or two to answer. Submitting an application puts into motion a time-sensitive process that involves completing programming challenges, taking timed online quizzes, and interviewing with school staff through video chat. While you may have some flexibility in scheduling when you undertake these tests, you probably won’t have the time to familiarize yourself with a topic that is completely new to you. Fortunately, schools will usually tell you exactly what they want you to know before you apply and some, like Metis, even have practice assessments you can use to gauge your readiness for admissions. I highly recommend studying topics like linear algebra, calculus, statistics, and scripting (generally coding challenges are in Python) and evaluating your progress with practice quizzes before you apply. Thanks to my preparation, I found both the Galvanize and Metis admissions tests to be challenging but well within my understanding of the material. While I had plenty of prior experience with Python, I had not studied any of the math topics in many years and would have fared far worse on those sections had I not spent a considerable amount of time reacquainting myself with them. If you also feel the need to review the math or statistics fundamentals, I recommend the sites Math is Fun and Mathopolis, which work in tandem to provide simple lessons and corresponding challenges to test your knowledge. For a more advanced understanding of math topics, check out 3Blue1Brown on YouTube, which includes entire series on linear algebra and calculus. A great book for beginners to coding is Learn Python the Hard Way, which should more than prepare you for admissions coding challenges. I utilized all of these resources and found them to be extremely helpful, but by no means is the list exhaustive.
Even though I felt prepared for admissions, I was surprised by the speed and intensity of the whole process. While I was successful in finishing the bulk of both applications on weeknights after work, in retrospect, waiting for the weekend would have been a smarter approach. Trying to complete a 48-hour coding challenge in the free time a full-time job affords is certainly not impossible, but it can add a good deal of unnecessary stress. Saving assessments for the weekend would have allowed more uninterrupted work time and fewer lost hours of sleep. For both Galvanize and Metis, successfully negotiating the online tests and coding challenges ushers in the final round of admissions testing: one or more online interviews with data scientists affiliated with the school. The interviewers are there to serve as a hybrid of test administrator and benevolent guide if you get stuck. I found the interviews to be friendly and casual, but simultaneously nerve-wracking, and recommend studying any suggested materials and more beforehand, particularly if you don’t like being asked to solve problems on the spot. I spent an estimated ten hours working through each program’s admission process from start to finish, not counting time spent studying.
Once you complete all phases of the application process you may have to wait up to a week to hear back from the school’s admissions office with your results. If you are admitted you’ll have to sign an agreement and make a deposit, usually within a week, in order to secure your seat in a cohort. I paid my deposit a little less than four weeks after filling out my first application, but that timeline could certainly have been made shorter if I hadn’t been simulatenously navigating two admissions processes. An advantage to applying to multiple schools at the same time is that if you get admitted to more than one you’ll have the luxury of being able to pick whichever you think is best. Alternatively, if you apply early enough and don’t get into your first choice of program you can try to get into a different school that has a cohort starting around the same time. I have also heard from admissions officers at various schools that early applicants have a better chance of being accepted due to class sessions having more open spots and fewer candidates farther out from their start dates. Some programs require several hours of work to be completed before the first day of class, and early admission affords you a head start on any pre-bootcamp assignments or extra studying you might want to undertake.
Hopefully this post sheds some light on the data science bootcamp admissions process and gives you an idea of what to expect if you decide to apply to one. If you want more details or have any other questions, feel free to email me at [ lukaswadya@gmail.com ]. Thanks for reading!
TL;DR
- Admissions can take a while, consider limiting your number of applications if you’re working full-time
- Study before applying because you may not have enough time to do so once you start the process
- Applying early gives you more flexibility and may make getting admitted to a competitive program easier