Engineering to Data Science
For a very long time, a career in data science seemed like a parallel universe that was so close I could almost touch it, yet just far enough away I wasn’t sure if it was attainable. I was working in a very different field (audio-visual engineering) but I was consumed with a curiosity about working with data. This is a short summary of how I bridged the gap between engineering and data science.
When I first started studying mechanical engineering at Texas A&M University, I had no programming experience. At the time the university pushed MATLAB as its primary tool for numerical analysis, and to put it gently, I was not a fan. At least not at first. A few semesters into college I finally admitted to myself that life would be easier if I learned how to utilize this new tool, so I set aside at least a few hours every day to put on some headphones and just code.
The first few weeks admittedly felt like pulling teeth, but slowly as I learned more about writing MATLAB code, something clicked. Solving problems and finding insight through clever programming lit a fire in me, and coding went from something I dreaded to something I looked forward to and sincerely enjoyed. Having code that actually ran was invigorating, and having code that actually did what I intended was even better. In a major that isn’t necessarily coding intensive, I had done something few mechanical engineering students accomplish: I had become the MATLAB guy.
Upon finishing my degree, I ended up putting coding down for a while and entered the field of audio-visual engineering. I traveled the country to work in venues and event spaces, and had a lot of fun doing it, but in the back of my head I always had the urge to get back into coding and analysis. I would often joke that I should have studied computer science as an undergrad because I connected so heavily with coding. Eventually, I think I said those jokes enough that I felt the need to research what kinds of careers might involve solving complex questions and finding insights through code. Naturally, this research led me to data science, and through that, Python.
I started teaching myself how to code Python out of curiosity, but that curiosity quickly spiraled into a full blown obsession. I knew from experience that I connected very closely with this kind of work, and now that I had discovered the tools necessary to break into the field I wanted to soak up every ounce of information I could find. The tools were new, but it all felt like a direct offshoot from the tools I had used before.
I studied as much material as I could from free resources, then invested in some Python books and a CodeCademy.com subscription to expand my knowledge. When I had dove deep enough into Python to be comfortable with some common libraries such as Numpy, Pandas, and Matplotlib, I finally felt like I had connected all of my past experience to this new path. Just about anything I could do in MATLAB, I could now find a way to do using Python. My focus could finally switch from bridging these two parallel universes together to exploring and mastering this new realm called data science. I decided to really take the plunge and look into data science bootcamps. All signs pointed towards the Flatiron School, and at the time that I am writing this I am a few weeks into their full time online data science program. It is a whole new world, and I am beyond excited to continue to learn, explore, and move forward.