Bootcamps — Data Science versus Programing

Bryan Combs
Sep 6, 2018 · 3 min read

The current technology boom has many people rethinking their careers and considering moving into the technology space. How does one know what space fits best? I am going to talk a little bit about my experience in two sectors and how I saw them fit with my personality and maybe that will give you some insight into what you might want to consider.

I spent some time in immersion programs in backend programming and data science. I want to talk about initial impressions and styles to give some context to the differing atmospheres present in the courses. I first started a backend programming immersion program. I determined I wanted to learn programing because I had always enjoyed the logic of programming dating all the way back when I was doing a basic class in high school. Also, I had not been in the work place in quite some time and figured a new start would be the best way to reenter the job market with new skills.

Coding was a little bit of a shock to me. I had been used to some freedom and flexibility in my life and i remember the first project assigned was a program to validate credit card numbers. The project had a specification sheet, and I followed all the rules on the project specifications. However, I changed small things like variable names to make them more intuitive to me and put in some individual touches. I remember one of my cohort mates looking at my code and commenting that I should not rename the variables, but I explained why I did it and he did not press the matter. I the evaluation the instructor feedback was also about the renaming of variable. It became clear really quickly that you do not change the “spec”. If something is in the “spec” it is in your program. Coding is not the place to individuality and flair.

I ended up leaving the program as it did not feel right to me and fast forward a few months to my first week of a Data Science immersive. I had some history with Data Science as I studied some statistical methods in graduate school. The coding language Python is extensively used in the boot camp I choose and early on we were coding with the instructor. We had a “spec” for the coding problem in the lesson, and the instructor asked us to send him our code on a problem. I, having been properly conditioned as a programmer, followed the “spec” to the letter, and my outputs were those directly specified in the spec. However, in the data science environment when some of the students changed the code and the output from the “spec” and the instructor laughed and praised the originality the students. He was happy that people had customized their output to reflect their personalities.

While the boot camp experiences have much in common, long hours and stressful conditions. I do think that this one difference reflects much of the difference between coding and data science. Coding is strict and formatted because programs have to interact and function with each other. Meanwhile data science tends to be more exploratory without the downside of creative license in the output. There is plenty of creativity in code, but it is in how you get to a specific destination, not the destination itself. Problem solving takes on a very different perspective. So before jumping into one of these boot camps it might be worth your time to explore the type of person that you are and how you work best.