Data are everywhere. From political polling on the nightly news to tracking airline prices and deciding when to lock in a mortgage rate, our lives are shaped by data. Data science, the art of collecting, analyzing, and interpreting data to find patterns, answer questions, and make better decisions, is no longer a specialized technical skill. It is now a basic form of social and financial literacy.
Yet in most schools, data science is handed off to the math department, as if its relevance ends there. This is a mistake.
As a high school math teacher of over 25 years, I see firsthand how naturally data science fits into math instruction. It brings abstract concepts to life and gives students meaningful entry points into mathematics. Math becomes less intimidating while showcasing its everyday practical application—comparing college tuition trends and return on investment, navigating healthcare, making sense of current events and news media, and exploring the unique interests of students in informed, meaningful ways.
But when we keep data science confined to math classrooms, we limit its power and prevent students from gaining a broader, more nuanced skillset needed to fully navigate the world around them. It belongs as much in a government classroom analyzing election results as it does in a science class tracking bat migration using data from the National Library of Medicine. When students work with real datasets, they do more than compute. They begin to question narratives, identify bias, and draw their own conclusions—qualities needed to be engaged and responsible 21st century citizens.
Imagine a math class exploring interest rates using Zillow’s Home Value Index, which tracks home values by ZIP code over time, alongside Freddie Mac’s historical mortgage-rate data. Students analyze how changes in home prices and borrowing costs have affected affordability across generations. The math teacher then shares the project with a social studies colleague, whose students examine how housing policies and demographic shifts have shaped communities over time. An environmental science teacher joins the conversation, guiding students as they investigate the environmental impacts of development and the challenges of balancing housing growth with responsible resource use. Finally, in English class, students synthesize the data, research, and perspectives they have gathered to develop evidence-based housing policy proposals. They present their recommendations to local leaders or submit them to elected officials, hoping to influence conversations about housing affordability in their community. What began as a lesson on interest rates becomes an authentic, interdisciplinary exploration of a real-world issue, one that teaches students how data can inform decisions, shape policy, and strengthen civic engagement.
While this type of learning may sound difficult to implement, it doesn’t require schools to reinvent the way they operate. In fact, it can start with something as simple as a shared digital space where teachers exchange datasets, project ideas, and successful classroom activities. With teachers willing to collaborate, encouraged and supported by leadership, students can explore the same dataset through multiple disciplines—making meaningful connections and thinking critically in a way that no single classroom could provide.
Additionally, the most meaningful data work happens at the intersection of disciplines, not in silos. A history teacher can guide students in analyzing demographic trends. A science teacher can help them interpret environmental data. An English teacher can use tools like Voyant (a free, web-based text analysis and visualization platform) to analyze word frequency in literature to bring greater appreciation and understanding to an important text and the author’s style. These tools provide not just information, but insight into the larger, connected world in which we live.
For this vision to become reality, school districts must create the conditions that support interdisciplinary learning. This means providing flexibility within curriculum frameworks, offering opportunities that bring educators from different content areas together, and encouraging cross-department collaboration. Teachers, in turn, must be willing to partner with colleagues outside their department and embrace the vulnerability that comes with not being the expert in the room. Meaningful interdisciplinary work requires educators to become learners themselves, modeling the curiosity, adaptability, and lifelong learning we hope to cultivate in students. When collaboration is embedded at every level of the system, students begin to see data not as an isolated topic but as a powerful tool for understanding the world.
When students enter the real world, disciplines do not remain separate, learning does not stop at graduation, and collaboration is essential for success. Problems are rarely confined to a single field, and data are woven into nearly every decision we make. To equip students for that reality, we must teach data science the way it exists in the world: everywhere.
