Why did I decide to learn data science?

My Journey into Tech

In college, I thought I was going to be a stockbroker on wall street. I loved taking classes like “Financial Markets & Institutions”, “Portfolio Management”, and “Investment Practicum” and I really did enjoy learning about the stock market in a classroom setting with the hopes of making the big bucks once I graduated. Two things happened that really shifted my focus. First, I graduated in the middle of the Great Recession when it was hard to come by any job, let alone one on Wall Street which was in a free fall at the time. Secondly, shortly after graduating, I read a hilarious book by Michael Lewis called Liar’s Poker that illustrated Michael’s life as a bond salesman in the 1980s at a now-defunct investment bank named Salomon Brothers. His description of the culture made me completely turned off to a job in that world. My next step logical step? Defer my college loans and move to Prague to teach English for a year.

Prague was fun, and I did a lot of traveling but knew from the start that teaching English as a foreign language wasn't for me. The cheap beer and the traveling were for me though. I came home and tried a few different jobs including as a valet (met some cool rock stars), an operations team member for a financial startup (answered a lot of phone calls), and as a loan officer (sold three loans the year I was there, two of which were to family members). After my illustrious career in mortgage lending, I landed a three-month contract for a tech start-up and it was during that time that I decided to take an online SQL course. Right away I was hooked. I was learning a new skill and it felt like every SELECT statement that ran without errors was magic! I immediately enrolled in a long-term continuing education program that would teach me even more skills in business intelligence in the evenings while I worked during the day.

Finding out about data science

While continuing to take business intelligence development classes learning about data analytics, I landed a contract at a big tech company supporting sales teams.

One day at my new role I came across a chart that looked like the one you see below from Gartner. It’s showing the maturity of analytics for an organization that handles lots of data. Having started taking classes, I was familiar with the first two boxes, but the words “Predictive” and “Prescriptive” in the last two boxes sounded cool and foreign to me. I realized my journey had just begun and I had a long way to go.

What excites me in the field

It’s been several years since discovering that chart and I feel like I am in a much more familiar place with data science, but still have a long way to go (there's a theme here). I have had a few roles as a Data Analyst and have completed a lot of cool online courses through Massive Open Online Courses (MOOCs). I have also completed a year-long course in R for statistical analysis that introduced me to machine learning concepts and how to go through the process from gathering data to eventually predicting outcomes with the data. I feel like I have come a long way from my first SELECT statement. That may be why I like the field so much because you can't afford to stop learning or you feel like you are being left behind.

I find myself fascinated by topics in data science that sound really hard to learn, particularly Deep Learning. I just think it’s the word “Deep”. It sounds difficult! I took a great series of courses online by a Stanford professor named Andrew Ng called Deep Learning Specialization and it humbled me quickly. Building and training neural networks is a specialty on its own and even learning the vocabulary of it can be intimidating (just thinking about the word “backpropagation” makes my head spin but in a good way!). It reinforced that I need to skill up to understand a lot of what Andrew is lecturing about and how deep learning is implemented using Python and a number of other tools.

Finding Flatiron

I realized that to skill up in the most efficient way, I should try a Bootcamp. I also found myself getting offered a lot of PM roles in areas that did not interest me so I felt like I needed a reset in my job search. I researched a few schools, but Flatiron School was the first school I talked to about the program, and just stuck with them until I was fortunately accepted into their Data Science Bootcamp. I figured I could try another school if it didn't work out, but everyone I met at Flatiron throughout the process was positive, informative, and passionate about what I wanted to accomplish.

My hopes for now

I think at the core of my interest in working with data is the satisfaction I get when I have figured out a solution for a data problem. It’s an awesome feeling and one that can happen multiple times in a day while you are working with data. I hope to continue to develop my Python programming and other data science skills to land a role in the future working on machine learning projects. It’s cool to know the last two boxes in the Gartner chart are feeling less foreign.

Careers in the tech industry can be really hard to navigate, but I have found what I am passionate about learning within the industry and I hope I am here to stay, that is unless Michael Lewis writes another account of his experience in the tech field that changes my mind…