Why Data Science?

  As a kid and a teenager, I was very active. No matter what season it was, I was watching and playing a sport. Sports have been a very large influence on my life and for the longest time, I wanted to be a college athlete. In high school, I honed in on just playing baseball and my goal was to play at a Division 1 school. The constant performance pressure of playing in front of scouts my junior year mentally changed my outlook on the sport I used to love to play. However, that same year I was taking an AP statistics course that was very intriguing to me. Even though my passion for playing the game had changed, I was now analyzing the sport through a different lens. I would look at the iPad which tracked stats after games viewing the advanced stats and trends. I wasn't only interested in baseball and my performances. I loved watching basketball, football, and golf as well!     While watching these sports, screen graphic pop ups would always drop in random facts and very niche statistics. I always thought “Where are they getting this information from”? Well, they were coming from the company's sports data scientists who submitted random stats relative to the team or game history. I ended up graduating from high school and then majored in the most general major in college… Communication Studies. I had no aspirations in college and not even a glimpse of what I wanted for my future. I got a job post undergrad working at a pharmaceutical agency as a digital project manager. I ended up leaving the job because I wanted to do something more than just delegating tasks to my team; I wanted to work in the “trenches”. After leaving, I went on some soul searching of what I liked and what piqued my interest. I of course thought about sports and how I could get involved in that industry. I wanted to challenge myself and be able to use my mind. So I was set on learning data science and sports analytics. Many different aspects of sports analytics are of interest to me. In college, I was introduced to the very addictive but interesting sports betting. How does one create the odds for a certain winner to be determined, how do they predict how many points a player is going to score in a given game and how do they predict how many wins a team will finish with when the season has not even started yet? Data scientists for sports gambling companies use predictive modeling and include relevant statistical measures that will help them produce odds and predict future outcomes. It is inspiring that people can do that and I want to be able to do the work behind certain bets and be able to predict outcomes. Player performance is another area of sports data science that is very interesting and has had a big influence on the game. Teams track players' movements, heart rates, speed, and other biometric measurements to figure out a player's weaknesses and strengths. Whether it be behind the sports betting books or on the practice field data science has become intertwined with sports and that is something I want to be a part of in the future. I chose Flatiron because of the reputation it has to produce results. Upon looking into data science boot camps, I was going through the website for Flatiron and it showed me the past year's analytics and the very positive career success after going through the boot camp. I also chose Flatiron because I know several alumni who have achieved in the workforce post-graduation. They told me that they were job ready because it was a very rigorous course and taught them everything they needed to know to get an entry-level job.