It all started with trains. My grandfather has maintained an interest in railroads and model railroads since long before I was born. Spending time with him when I was young, we would listen for the characteristic rumble of of freight on the CSX line at the end of the avenue. It was our cue to go outside and count the locomotives and freight cars by type, paying close attention to the markings and contents of each. Beyond categorizing trainsets, I also found the rail networks themselves of curiosity. As I traced hatched freight lines across old AAA maps, it inspired me to build more complex model train layouts. There was something great about getting lost in a project, then stepping back and trying to decipher it
Later on, around 9 or 10, I went through a stint of architecture mania. The same intricacy that drew me to railroads now drew me to buildings, tunnels, chambers, and halls. Star Wars cross-section books had no small part in this!
At 12, following my longtime love of all things Lego, I joined a nearby FLL robotics team. Solving materials handling challenges with even the simplest robot required many coordinating systems (drive-train, sensor guidance, and varying attachments), all controlled by software. For the next 6 years of competition in various leagues, I gained a frustrated appreciation for the complexity of solving technical problems. At some point, my high school team decided to stop reinventing the wheel and use data to improve our designs. With a small army of scouts, we used a spreadsheet to rank all of the teams in our league by how well they performed in different competition categories. The aggregated information guided later design decisions and helped send our team to the world competition twice.
Interestingly, the process of analyzing and comparing robotics teams often took precedence in my mind over actually engineering our robot. Building data sets and analyzing them with simple spreadsheet graphs felt like solving a grand mystery. My ADD brain was also relieved to find some external order to sort the internal information soup. At high school graduation, I had never heard the term ‘Data Science’, but in some sense I was already doing it. In 2018 I began learning the magical language of Python, which I soon discovered was integral for any modern data scientist. Now I’m two months into the part-time data science program at Flatiron School, and I couldn’t have made a better choice. After many years on a winding road of interests and aspirations, I’m finally developing the skills necessary to scratch my curious itch and see the world in a richer, more colorful, data-oriented way.