Why am I here?
OK, I know the title sounds like I’m having an existential crisis, but I swear that it was just clickbait, please keep reading!
This introduction is not going to be standard. This is because I am talking about myself, mostly in the first person, and that makes this whole thing seem more like a casual conversation. This is a summary of my journey to finding my passion for data science. It might be a little boring, or a little monotonous, but it’s been a wild ride, for me, and I’m so glad that I get to share that today.
As the case with most Data scientists, the journey to make it here was a long and winding one. There was no direct path to this profession in university or famous data scientists to aspire to be. The struggle was to filter what interested me; every subject interested me. I loved Maths, Biology, Chemistry, History, Economics, Medicine and English. In university, this problem just extrapolated, every subject became even more interesting the more knowledge I accumulated. It got a little overwhelming until I realised, I didn’t have to choose; there was a common thread that connected all my passions: data.
My love and passion for data can only be illustrated by describing what data means to me. Data is the key, it’s the codebreaker, the dictionary (the Oxford kind, not the python kind) that allowed me to see the world through a totally different lens. Suddenly, everything had meaning, every aspect of life was a potential input or output, and the world was just a canvas for all these beautiful symbiotic relationships. This discovery really excited me, and I dove more into what tools I would need to develop my skills in data science. During my double bachelors in Econometrics and advanced science, I had been exposed to many different applications and languages used for data analysis. MATLAB, R, Stata, EViews, and SPSS to name a few. However, none of them offered the flexibility to transcend to a universally accepted method of data analysis.
This was when I was first introduced to Python programming. I quickly discovered that with great flexibility comes great difficulty. As I struggled to teach myself how to navigate and apply code appropriately, I began to lose hope that I could be proficient enough to really explore and understand how to use data to gain insights. However, I couldn’t give up, I explored other languages like SQL and discovered the true elation of running a code line and not seeing an angry red error. While I was exploring, the world was catching up to the importance of data to the technological advancement of every industry. The year I graduated was the year data exceeded oil in terms of worth per unit. This was exciting to me because I finally saw an opportunity where I could be doing what I loved.
I started my master’s in data science; with the objectives to overcome my trauma induced fear and dislike of python programming and really find my niche within the ever-growing space. Unfortunately, the timing of my pursuit coincided with a global pandemic (yay!). To make matters even more challenging, I was living in the US and my lectures and tutorials were all in Australia. The time zone juggling and just being exhausted all the time really hindered my ability to full grasp all the concepts and technical nuance I needed to apply my skills without help. Most of the programming was also taught on a platform, so I got no experience in working with commonly used Python integrated development environment (IDE)s. All of these issues combined made it really hard for me to succeed when I was trying to reinforce the topics I learnt in lecture in my own time.
That’s when I knew I needed a different approach: I had a passion and penchant for data, I just needed to facilitate a learning experience that would make me a better programmer. It didn’t take too much research to realize that a coding bootcamp was going to be my best bet. This first week at flatiron has been eye-opening; I’ve realized that python programming is so much less intimidating when each step is broken down. I’ve also realized that there is no true replacement for a group of people learning together and helping each other succeed
To conclude, I want to explore a little on how I plan to apply everything I’m going to learn here at the Flatiron. I was in a pharmacology lecture when I first realized that an individual’s genetic code can determine how fast they metabolize medications. This absolutely blew my mind. Even though, scientists are still identifying which genotypes correspond to which phenotype: one day this data set will be complete. I think the combination of individuals genetic code and health data could be the foundation for machine learning algorithms that can predict the best medication for that person. Theoretically, this has the potential to maximize therapeutic benefits and minimize unwanted side effects. As both a student of Pharmacology and Data Science, this new generation of innovation is especially exciting. I can’t wait to be a part of it!