Rethinking the Role of Data in Math Education

Image courtesy of Harvard Data Science Review

When students ask, “Why do we need to learn this?”—data might be the most convincing answer we have.

Across fields like healthcare and housing, our world is flooded with information. But in many K–12 math classrooms, data is still treated as a side topic, not a central part of learning. That comes with real consequences: between 2019 and 2022, eighth-grade scores in data analysis, statistics, and probability dropped 10 points—what some say is equivalent to an entire grade level of lost learning (2022 NAEP Report Card).

So how can schools integrate data science into math instruction—and what’s holding them back?

Why Data Science Matters

We often say “students need data skills.” But what does that really mean?

First, it’s about jobs. The data economy accounts for one in every four jobs in the U.S., spanning sectors from logistics to healthcare to public service (Salerno and Steemers, 2024). At least one data skill is required by 35.9% of jobs in utilities, 32.1% in manufacturing, and 20.5% in education require data-related competencies (Sukol, 2024). Yet most students don’t encounter data science until college—if ever. Since only about a third of Americans hold college degrees (Salerno and Steemers, 2024), that leaves many unprepared for a workforce shaped by data.

Second, relevance boosts engagement. Kevin Dykema of the National Council of Teachers of Mathematics says students “are eager to make sense of the world,” but often don’t see how school math connects to real life (Dykema, 2024). This disconnect is reinforced by curricula that prioritize procedural fluency over conceptual understanding. Too many students master algorithms for problems like synthetic division or conic sections, she notes, without ever grasping why these tools matter or how they apply beyond the test (Burdman, 2023). As a high school student who’s taken AP Calculus, this resonates deeply. I’ve solved countless derivatives and integrals, but I’ve rarely applied the skills beyond problems such as finding the volume of a donut.

Data science classes, on the other hand, often focus on inquiry, interpretation, and argument—skills that apply across disciplines (Dykema, 2024). Take The Pudding, for example, a digital publication that uses data to create visual essays on topics like loneliness epidemics or influencer apologies. It shows how data can be used to tell stories—not just solve equations—and that’s exactly the kind of relevance students are looking for. Data science education also fosters a sense of agency for students: in Dale Perizzolo’s class at Adolfo Camarillo High School, for instance, students designed their own project tracking cellphone usage with Google Forms and posed their own research questions—like whether using social media during class affects comprehension (Salman, 2024). This kind of independent investigation exemplifies how data learning connects directly to students’ lives.

Third, data science supports interdisciplinary thinking and civic engagement. Unlike traditional math—often limited to formulas and abstract symbols—data learning is naturally cross-cutting. Students work with real-world datasets on issues like climate change or housing, building what researchers call quantitative civic reasoning: using data to make sense of and contribute to social conversations (Gargroetzi et al., 2021). As Shereen Tyrrell of Burlington Public Schools put it, “Once we brought data [into the classroom], the science teachers joined us. The business teachers joined us. The social studies teachers joined us. It was not just for math majors” (Data Science 4 Everyone, 2024).

Why Data Science Education Is Controversial

Of course, implementation isn’t always easy. Right now, early exposure to data science is mostly limited to well-resourced schools. The 2023 National Academies report says data science programs exist in just 6% of high schools, with only 3.6% of students enrolled (Harding, 2025). If it stays confined to AP or specialized programs, it could deepen—not narrow—opportunity gaps.

There’s also debate about rigor. According to a report by the Cal Alumni Association, in California, data science was briefly accepted as an Algebra II substitute for UC admissions—until some STEM faculty pushed back. Critics like Berkeley’s Stuart Russell called it “a misleading path to nowhere” (Alcantara, 2024). Stanford’s Brian Conrad criticized the state’s math framework for promoting what he called “algebra-lite” alternatives (Alcantara, 2024).

But others, like economist Steven Levitt, argue that data science is a rigorous field that blends computation, statistics, and real-world thinking—and that limiting access only deepens inequality (Levitt, 2023). Pamela Burdman (2025) notes that most California students taking data science already complete Algebra II. She further argues that “many students who take Algebra II don’t learn much of the content,” nor does procedural mastery guarantee success in college math (Burdman, 2023).

At the heart of this debate is a bigger question: Do we define rigor by content coverage, or by students’ ability to model, interpret, and think statistically? The answer matters. If done poorly, data science could become another sorting tool. But if done right, it could help make math more inclusive and meaningful.

What This Means for Schools

So what’s next for schools across the U.S.? Based on recommendations from the national coalition Data Science 4 Everyone, there are a few concrete steps districts and schools can take:

• Offer data science electives

• Embed real-world data projects into core math and science courses

• Train teachers across subjects to teach data fluency

• Create space for community conversations and planning

Teachers can start by introducing data-rich assignments, talking with students about why data matters, and exploring free tools like CODAP or DataClassroom. As national standards are expected in early 2025, school districts have a chance to lead—building a more modern, relevant, and equitable math experience for all students.

Zooming in on One State: How Massachusetts is Moving Forward

Massachusetts is trying to make data learning both civic and personal. Since 2021, the Innovation Pathways to Data Careers (IPDC) initiative has helped high schoolers build data skills through interdisciplinary courses like:

• A Civics+Data module focused on local issues

• Data-infused Algebra II/Math III units

• Electives in Visualization+Data and Python+Data

So far, 55 teachers and over 1,000 students have taken part. Students have used local data to explore topics like housing inequality, and more districts are joining in (Data Science 4 Everyone, 2024).

Meanwhile, TERC’s Civic Data Project is helping middle school teachers design lessons using real civic datasets—like public transit access or eviction rates—to build students’ data fluency and engagement.