Shaping Futures Through Data Science

As a college student, getting to know someone new almost always involves asking them about their major. Whenever I’m asked about what I’m studying in college, my answer inevitably incites more questions. What is data science? What do you do with it?

I first learned about data science in high school when I was exploring college majors. As a firm believer in learning about anything and everything, I wanted a major that accommodated my interdisciplinary interests. Upon hearing this, my high school counselor suggested that I look into data science: an emerging field at the intersection of statistics and computer science.

Data science harnesses statistical methods to process data into insights. The entire premise of data science is problem solving – as data is increasingly present in today’s technologically driven world, it serves as a powerful tool to tackle various issues. Problems in data science revolve around modelling, predicting, estimating, or combinations of all three. Modelling constitutes building statistical frameworks to find relationships in the data. Predicting utilises the data to glimpse into the future and forecast what’s to come. Meanwhile, estimating involves making educated guesses about groups of data. In short, data science is the driving factor behind most modern-day decision making processes.

My first ever data science project was a modelling problem: how does a country’s educational conditions shape its response to a global health crisis? This question had long piqued my interest. During the COVID-19 pandemic, I saw first-hand how misinformation spread faster than the virus itself, fuelled by fear and uncertainty. I wondered if this undermined public health efforts like vaccination or social distancing, which in turn would lead to more COVID-19 cases. To analyze this, I collected global COVID-19 statistics as well as literacy rates, building models to see if any patterns emerged. Even if the analysis results weren’t conclusive, I was introduced to central tenets in data science: curiosity. No question was too trivial to investigate, because the value lies in asking, exploring, and thinking critically about the data.

Another memorable moment in my data science career was actually not very technical at all. Earlier this year, I had the pleasure of taking a digital humanities class – one of my favourite classes at UCLA thus far. While discussing a dataset on prison records from the 1800s, the professor inquired: “What do you see?” Voices intermingled with one another, pointing out information easily gleaned from the dataset: names, ages, criminal convictions. After a while, the professor asked again: “What don’t you see?” This was such a simple statement, but it completely revolutionised the way I think about data. Instead of focusing on what I can do with the data, I started considering what wasn’t present, and why that was. In doing so, I dove into background knowledge, learning about how bias and power could affect data. Understanding the data beyond itself expanded my creativity.

Data science also taught me the value of good communication. Near the end of my junior year, I participated in DataFest at UCLA, the largest data hackathon in Southern California. Along with my groupmates, I was tasked to analyze a commercial real estate dataset from a real estate advising firm, which contained thousands and thousands of entries. Scrolling through the endless data points, I was daunted by this gargantuan task. How exactly were we going to make sense of this data?

Instead of meeting complexity with more complexity, we decided to go back to the basics. What questions could this dataset answer? What insight would be meaningful? We focused on locating hotspots in Los Angeles for industries such as finance, legal, and tech. A hotspot meant that a significant cluster of companies within an industry had offices in the same area. These hotspots could help the firm make decisions on where their client should choose their new office.

After analyzing the data, we prepared for our presentation to both peers and the judging panel. We knew that models were useless if no one could understand them, so we prioritised clarity by using everyday language and intuitive visualizations in the narrative we delivered. Our hard work came into fruition: we won the Don Ylvisaker Best Insight Award, which honored the best team in delivering the strongest key insight from the data. The experience cemented a crucial lesson about the end goal of data science: no matter how complex the model, how brilliant the finding, it serves little purpose if you’re unable to communicate it clearly.

Looking back, my data science journey has undoubtedly shaped the person I am today. Data science pushed me to jump straight into problem solving, approaching data both critically and creatively. Not only has data science sharpened my logical reasoning, it has also enabled me to be a better communicator, breaking down high-level ideas into concepts anyone can understand. These habits extend far beyond coursework, applicable in situations I encounter everyday.

This is exactly why I believe that high school students will benefit from learning data science. In a society increasingly driven by data, there is no doubt that students will find the need for data analysis wherever their future paths may take them. Both the technical and the soft skills the field requires – coding, statistical reasoning, critical thinking, creativity, communication – are universally valuable, shaping a refined way of thinking that transforms how students see the world.