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It is impossible to imagine a future that does not have data science embedded in our daily lives. The field combines statistics, coding, and critical thinking to extract meaning from data, making it a desirable skill across industries. Yet most American students graduate without any tested knowledge of it. One high school teacher in Wisconsin set out to change that.

After teaching math for years, Nicolle Dexter completed a data science graduate program. To her, the growing interest in the field was not a temporary trend but a discipline shaping our future. The evidence says she was right. Companies rely on analytics for revenue projections, healthcare professionals use longitudinal data tracking to diagnose patients, and even artists and influencers depend on engagement, streaming and sales data.

Dexter pitched a pilot course to her school district as an alternative to Algebra II for students who had completed Geometry. District leaders welcomed it as a way to modernize math instruction. Soon her course at Holmen High School had 12 students enrolled.

Students signed up for different reasons. Jackson Dwyer, one of three seniors I spoke to, had already taken AP Statistics and Computer Science; data science let him “combine those fields in a different way.” Maria Suarez did not want to take AP Statistics, which “sounded too dry,” but she knew applied statistical concepts would be fundamental to her planned career in chemistry. Owen Shaw thought the course was interesting and applicable to college. All three cited one shared reason: “We like Ms. Dexter!”

Dexter designed a new approach to math education from scratch. She didn’t try to make it match with what they’ve experienced before in math classes. Instead, she modeled it after what her master’s degree classes looked like. No tests, no worksheets, no typical homework — the course is solely project-based, moving from data collection to data visualization to hands-on model building.

Students collected data about their own behaviors to find patterns, drew candy from a bowl to understand probability and built histograms using Pokemon card statistics. Unsurprisingly, each student picked topics that interested them to learn core principles of data science. Maria’s favorite project analyzed lobbying expenditures for Wisconsin’s congressional delegation and found that a small number make up a large majority of contributions. Owen categorized the notifications he received on his phone and discovered that promotional college emails made up a disproportionate majority.

The course structure breaks subject silos. To complete projects, students routinely combine algebra, data analysis, subject area knowledge and coding. Perhaps that interdisciplinary nature is why all three students concluded the subject does not feel like it belongs to the math department at all. “This is a bit of a different approach to math class than anything I have taken before,” said Jackson.

Dexter believes that conclusion is partly driven by a misconception. “I think that my students would incorrectly say that they do less math in this class, than in other classes, because they have a misconception about what math is,” she said. “It is not a procedural math that they could rely on in the past. Some of the uncertainty that is an important part of data science is what we are interested in.” 

That uncertainty was a novelty at first, but Dexter kept raising the difficulty until students had the tools to explore large data sets and find answers.

As the school year ended, Maria admitted, “This class has definitely shifted how I feel about the statistics side of math. It doesn’t have to be super boring!”

Implementation was hard on everyone. Students acknowledged the course required more work than others, especially learning Python syntax alongside analytical approaches to data. But they said Dexter’s approach was never about memorizing lines of code; it was about understanding the process of problem-solving. Her own biggest challenge was letting go of the teacher-led classroom model.

Arguably, that seeming chaos is a much closer reflection of real-world math than worksheets and equations. For decades, math and the way it’s taught  have not changed. Parents of students at Holmen High learned the same calculus concepts from textbooks no different from today’s.

Generations were taught to imagine math solely as a procedural science: analyze an equation, complete the right steps, get the right answer. The real objective — and perhaps the real challenge — of modernizing math instruction is making it applicable to students’ lives, taking math from its procedural roots to its creative applications. That is what Dexter set out to do  at Holmen High.

While states and districts continue to debate data science education, the real world does not wait. Instead of leaving data literacy on the margins of the K-12 curriculum as employers’ demand for it grows, policymakers, teachers and curriculum designers ought to treat it as a plank of the national effort to improve literacy. 

To be successful in the tech-heavy world of tomorrow, children will need pattern recognition, statistical analysis, and data storytelling just as much as numeracy and literacy. And Dexter’s classroom is a snapshot of what innovation can look like when teachers are empowered to teach for the real world.

The results, even in the small sample of 12 students, speak volumes. Jackson and Owen are headed college and into degrees in computer science and electrical engineering with a stronger background. Maria, who hopes to major in chemistry, is excited to take more data science courses in college. 

Put simply, if public schools take a leap to stop teaching the priorities of yesterday, students will become skilled-adults of tomorrow.

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