Choosing a college major: one girl’s journey into math and data science
Sometimes knowing how others made their decisions can help you decide for yourself
I have always been good at math. As a child, I never took my ability and interest in math seriously. I never knew anyone who studied math beyond high school, so the idea of majoring in math never crossed my mind.
When I started the international baccalaureate diploma program (IBDP), I fell in love with Math HL (Higher Level). I thought it was a beautiful and elegant subject, and I felt that I understood it and it understood me. As a part of the IBDP, I needed to write an Extended Essay (a 4000-word research paper) on a subject of my choice. I had rarely heard of people who choose to do their Extended Essay in Math. But my high school teacher encouraged me to take it up as a challenge and explore an area of math that I was passionate about.
I knew I wanted to explore an area that was new to me, that I hadn’t already studied before in high school. While bouncing ideas off a friend, it hit me that I could test hypotheses and explore certain factors that predicted a university’s tuition fees. I spent a month poring through the internet to understand the math behind multiple linear regression. In the process of producing my extended essay, I was exposed to statistics and data, two areas of academic inquiry that I remain passionate about.
On entering university, I was excited to take Data Science classes and further cultivate my interest in the discipline. My inexperience in coding in high school did not deter me from learning. After taking Data Science For Everyone (DS-UA 111), I knew I wanted to major in Data Science. I found that the class fed right into my passion for uncovering meaning through data. I felt intellectually stimulated by the topics, especially the counterfactual nature of causality.
When I took Introduction to Data Science (DS-UA 112) in the Summer of 2021, I was determined to give my all to obtain a deeper understanding of data science concepts. One of the first things my professor said in class was:
Data Science is a three-legged stool. One of the legs is programming. One is Probability. The third is Linear Algebra.
At that time, I had already taken a coding class in python and was familiar with several probability and statistics concepts. But linear algebra was new to me. I couldn’t understand why Linear Algebra was important enough to be given a whole leg. I brushed it off, happy to ignore its importance.
In Fall 2021, I finally dipped my toes into Linear Algebra! I was a little nervous about taking my first non-calculus math class in college. Soon, I started connecting Linear Algebra topics with concepts I already knew from vectors and calculus. I started appreciating the beauty of math once again. The class ended with us learning about singular value decomposition, and that’s when it hit me. I understood the mathematical intricacies behind Principal Component Analysis and many other things we learned about in Intro to Data Science.
I realized I wanted more than just knowing the steps outlining data science concepts. I wanted to understand the minutia. I think my interest in math led me to major in data science, and my interest in data science rekindled my interest in math.
Ultimately, when choosing your major, especially one as interdisciplinary as data science, it’s essential to ask yourself: “why do I like studying this?” Sometimes, the answer might surprise you. And lead to a whole new world of opportunities.
I’ve also found it incredibly helpful to try out the things I was curious about before committing to a major. Writing my Extended Essay in Math and taking classes like Data Science For Everyone and Linear Algebra let me test if I would be happy doing math and data science as a career.
If you’re facing your own decision — be it a college major, a career shift, or anything else — I highly recommend Decisive: How to Make Better Choices in Life and Work by Chip and Dan Heath and their 4-step framework to making life decisions. Check out this great infographic from subba.org for a visual summary of the book!
