I can approach this question similar to how one could approach data, or so I am learning. There are numerous ways and they vary in readability, depth, and finesse. So I will focus on readability, delve as deep as is necessary, and remind you that I am a beginner, both in blogs and in data. Overall, I choose to study data science because I want to be learning, I want to be challenged, I want to be proud of myself.
If I could have stayed in college forever I would have. My family would say that I tried. When I think back to the years I was most happy and fulfilled it is easy to pinpoint the years I was in college. The feeling of accomplishment gained from learning and growing daily is something I took for granted and sometimes even cursed while I was there.
Having graduated, my feeling of accomplishment from learning had to come from new job experiences or personal interests. Learning experiences in my previous jobs tended to dimish with time, some sooner than others. Interest in my work would die soon after I realized my time of learning was over. With the ever-increasing growth in data however, there will be no shortage of questions to ask, skills to learn, or methods to master. The ability to continue to question, to continue honing my craft, and possibly contribute to the field of data science is what speaks most to my desire to forever be learning.
I will face challenges as I pursue a career in data science. Of this, I am certain, because it is an everyday occurrence already. But I've never wanted to pursue anything that was not a challenge. I was a first-generation college graduate, I completed a summer research program at the University of Maryland, and I was admitted into a competitive Ph.D. program in Economics, and I taught 13-year-old girls Algebra, none of these things came easily. It was the challenge that drove me, that kept me interested, and made it feel worth doing. Although I am just beginning my journey I have already faced and surpassed challenges. These challenges have just been a missed colon, a typo, forgotten syntax, or not understanding what people around me were talking about. Regardless of how simple the challenges have been so far, they did their job, they made me doubt my ability. Surpassing them, though, strengthened my determination to rise to the challenges.
As I mentioned above, if I could have stayed in college forever I would have. So many find it confusing when I tell them that I dropped out of my Ph.D. program only two weeks in. This was 7 years ago now, and yet it crosses my mind almost daily. I had put so much time and effort into getting admitted to the program but once I got there I lost my nerve.
My classmates all had a higher academic background than me. They had already completed a master's or Ph.D. in another subject. I was the only student that had come in relying solely on a bachelor's degree. I was intimidated. Feeling intimidated coupled with the doubt that I chose the wrong path in pursuing economics instead of statistics consumed my every thought. Between the intimidation and the doubt, I chose to focus on the doubt. I convinced myself that I had chosen the wrong path. I convinced myself I had no passion for economics. I convinced myself I would regret continuing in my program. I convinced myself I wouldn't have felt this way had I chosen the statistics program instead. It was easier to say I had made the wrong choice than admit that I was an imposter. The truth is I dropped out of my program because I felt like an imposter. I've been looking for a way to prove confront those feelings again and instead persist past them. I can't think of a better field than data science to do exactly that. The ability to investigate, answer questions, and then present those findings, will not assuage me feeling like an imposter; but, continuing to do so regardless of those feelings eventually will.
I could have used this blog entry to talk about my passion for data, statistical sleuthing, or the amazing job market that will be open to me, but for me, this is more personal than that.
Top comments (0)