Those who are new to data science are recommended by data science blogs and data scientists to pursue side projects. These side projects should showcase your skill set and help you create a foundation for your portfolio. The job industry places a large emphasis on ones “research portfolio” as an important piece of landing a job. Despite this sound logic, I pursue side projects for different reasons..
As most of my friends can attest to, I often have moments where I exclaim, “Ugh, I hate R!” And the truth is, in those moments, I really really do hate R. I’ve come to realize that even though I love my masters program and my research, sometimes the work involved becomes tedious. If I focus solely on the end goal of perfection, programming is no longer joyous to me.
To remedy this, I pursue side projects. I have a running list in my journal of things that would be fun to investigate or build - a lot of these are related to other interests I have. For example, two that are admittedly pipe dreams are 1) a program that correctly identifies a Monstera deliciosa versus a Split-leaf philodendron (because those are different plants!) and 2) analyzing the beautypedia skin product review website because there are some ratings with which I strongly disagree.
The side projects that I currently pursue allow me to explore and build on various skills. But to me, the most important thing is that they allow me to work on a project with no risk attached to the end product. I work on my project when I want to and if they fail (and a lot of them have), I just move on to the next one. These side projects are the way that I remind myself that I love R, I love programming, I love building things and exploring different ways to answer my own questions.
