1, 2, 4, 8, 16, 32, 64, 128,...256,...512...
I once again paced around the room trying to see how many "doubles" I could do in a row. I was in second grade and I did not yet know what "powers of 2" were, but I loved playing with numbers in my head, seeing how far I could go.
Fast forward to today: I am starting my second week of Flatiron School's Data Science Bootcamp and I am loving the challenge. I will give a short(ish) explanation of how I got here.
College
In my college days, I was a mathematics major at Wesleyan University (go Cards!). At first, I loved evaluating integrals--it was like solving little puzzles, but in some ways more of an art than a science. Then things started to feel unfamiliar: math was no longer about "doing problems" in order to get "the answer." Rather, it was about understanding ideas. My homework solutions were now a series of paragraphs, sometimes quite lengthy, proving some claim or theorem to be true (or false). Algebra and Topology were fascinating, but in some ways I was more interested in the logic and methods used to prove or know things.
When I took Introduction to Programming in college (taught in Python!), I was delighted to find out that these sort of meta-mathematical ideas were very applicable to computer science. Understanding Boolean operators and symbol logic, as well as how to combine operators and create truth tables, was an essential skill. In math, proof by induction, one of my favorites, involves declaring a "base case", proving A to be true for the base case, and then proving that if a statement P is true for an integer n, then it is true for n+1. These two pieces combine to show that P is true for all integers n greater than or equal to the base case. Any programmers reading this will know that what I have just described is an infinite loop (for non-programmers, think about taking "wash, rinse, and repeat" literally), but the concept was very helpful for understanding while loops. This course was a preview of the way in which my mathematical interests could be applied in the tech world.
After College
The first several years of my post-college career have led me on a winding path to Flatiron and Data Science. Initially I was interested in the actuarial field. I took the first three exams for the Society of Actuaries' certification: I really enjoyed getting really deep into probability and annuities as well as learning the math behind calculating prices for stock options. Years later, I worked as an actuarial intern--this was my first introduction to data and data science. I was no longer learning theory--instead I was dealing with huge Excel tables of insurance policy premiums and digging into VBA and SQL code to determine how things were being calculated. This was interesting, but I was a bit awestruck by interns and actuaries who had a background in data science. They seemed like wizards to me. I would be fascinated to delve into the applications of data science to the actuarial field.
At a certain point in this journey I became interested in education. I worked in after-school centers and charter schools and took some postsecondary coursework in mathematics education. I was fascinated to learn, in addition to the statistics and programming content being taught in schools, that data has become essential to the field. Whether in academic studies, policy-making, or charting individual student-performance and in-the-classroom decision-making, data is ubiquitous. It is used to create an in-depth picture of a student or class's progress in many areas. Statistics regarding the performance of individual schools or districts is paramount for politicians and policy-makers. Quantitative studies rely on data and statistics to determine the best teaching practices as well as the effects of environmental factors on academic performance. While I found that classroom teaching was not my forte, the fascinating importance of data and statistics on the world of education has stuck with me.
Onward...to Data Science!
Life has no roadmap. No one can tell you with certainty what you should do or where you should go. At times I've felt like I'm walking along a mobius strip, traversing a winding and looping path and ending up in a place I've been at before, yet changed and reoriented by the journey. Recently I found myself at a transition point with no obvious next step. Yet, everything I mentioned above, all the signs and glimpses of my future, have gently pushed me into a new field: data science. It is up to me to trust my instinct, believe in myself, and rise to the challenge.
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