What if coding could solve your math homework? That’s not the kind of question you’d expect in a high school classroom, but it’s exactly what Introduction to Data Science (IDS) taught me to ask. My high school first offered IDS during my sophomore year. Marketed as an alternative to calculus for upperclassmen, I eagerly enrolled despite being younger. Having already completed every computer science course at my school, I was thrilled by the chance to further develop my coding skills. What I discovered was a course unlike any other—one that inspired my UC admissions essay, shaped my academic confidence, and laid the groundwork for my success as a UCLA computer science student.
By contrasting IDS with traditional math and computer science courses, its value to students becomes clear. I took IDS alongside AP Statistics and AP Calculus BC, and I can confidently say IDS uniquely prepared me for college. Traditional math courses like calculus focus on textbook-driven learning—digesting information and applying formulas. While this approach is valuable and well-supported in higher education, it contrasts sharply with IDS, which emphasizes real-world applications over rote memorization. Both methods are important, but IDS fills a prominent gap by equipping students with practical, transferable skills often overlooked in traditional curricula.
During my first quarter at UCLA, a professor asked our class to download a CSV file and import it into a Colab notebook containing pre-written code. Many students felt overwhelmed, whether by their first encounter with a CSV file or by the sight of code in the notebook. Thanks to IDS, I approached the task with confidence. I recognized the file format, understood the purpose of each code snippet, and appreciated the comments explaining the logic. Even knowing basic concepts like understanding the syntax of comments gave me an edge over peers encountering these concepts for the first time. The code was well-documented and I had the skills to navigate it. Learning these fundamentals in a low-pressure high school environment gave me a significant advantage over those grappling with them for the first time in a crowded college lecture hall.
Unlike AP Computer Science, which often attracts students already interested in programming, IDS invites a broader audience by presenting coding as an accessible problem-solving tool. This distinction is crucial. Traditional computer science courses can inadvertently reinforce the misconception that programming is reserved for the technically inclined. IDS dismantles this notion by embedding coding within a math curriculum, reframing it as approachable and universally relevant.
The course’s real-world applications make it particularly impactful. For high school students, “real-world” might mean something as relatable as analyzing Spotify Wrapped data. By grounding programming in familiar and engaging contexts, IDS demystifies it, fostering confidence and curiosity. It empowers students to view coding not as an intimidating abstraction but as a practical, essential skill they can use to achieve tangible outcomes, regardless of prior experience or career aspirations.
This accessibility has far-reaching implications. Coding is increasingly a baseline competency across disciplines, from life sciences to humanities. Yet many students struggle with programming requirements because they haven’t been exposed to it in a meaningful way. For example, my roommate, a neuroscience major, was tasked with using Python to run math modules in her introductory life sciences course. She didn’t even attempt the assignment before asking for my help, assuming it would be too challenging simply because it involved coding. IDS addresses this gap by normalizing coding as a universal skill. By integrating programming into familiar problem-solving contexts, it prepares students to tackle interdisciplinary challenges with confidence.
Beyond technical skills, IDS imparts critical lessons about data itself. In today’s world, data often carries an aura of unquestioned authority, treated as synonymous with evidence. IDS challenges this perception by teaching students to critically evaluate data sources, question methodologies, and recognize biases embedded in datasets. These skills are essential across fields, empowering students to navigate the complexities of data-driven decision-making with awareness and discernment.
For me, IDS served as a bridge between high school and college, turning abstract concepts into practical skills. It demystified data and coding, empowering me to approach both with confidence and curiosity. This foundation has been invaluable in my journey as a computer science major, where data literacy and programming skills are indispensable. Introduction to Data Science is more than just a class—it’s a transformative experience. By blending mathematics and computer science, it fills a crucial niche in education, equipping students with the tools to succeed in college and beyond. IDS demonstrates that data literacy isn’t a luxury but a necessity, empowering individuals to actively engage with the data they consume daily. It’s a class every student can benefit from, regardless of their career path.
About the Author
Amy Lloyd is a computer science student at UCLA with a strong passion for making data science education accessible to everyone. In high school, she served as president of the Computer Science Honors Society, where she launched outreach programs to teach middle school students about computer science. Amy is dedicated to integrating the student perspective into data science curricula, ensuring it is both engaging and relevant for all learners.