AMES, Iowa — When Eric Weber, professor and chair of mathematics at Iowa State University, talks about data science with future math teachers, he doesn't begin with code, algorithms or buzzwords.
Instead, he asks them to imagine the scientific method — form a hypothesis, collect data, conduct experiments — running in reverse.
"In data science, you don't start with a hypothesis or prediction," Weber said. "You start with the data that already exists — maybe numbers someone collected years ago, or information gathered for a totally different purpose — and you work backward. You look for patterns, connections or surprises in the data, and those clues help you figure out what questions you should even be asking. So, instead of testing a hypothesis, you're discovering one."
This definition is the basis for curriculum Weber and colleagues at Iowa State and the University of Northern Iowa (UNI) have designed to help prepare future math teachers to teach data science in high-school classrooms. Their work reflects a growing national consensus that data science literacy should be part of secondary education.
"Multiple professional societies in mathematics, statistics and mathematics education have released statements in support of teaching data science in high schools," Weber said. "But while high schools are being encouraged to add data-science courses, the teachers expected to teach them often receive little to no preparation."
In a new paper published by Scatterplot, the MAA Journal of Data Science, Weber and his co-authors argue that future math teachers are the educators best positioned to take on this role — but only if their training programs give them the tools to do it.
"Our goal is to help close that gap with the curriculum we've created," Weber said.
Weber's co-authors are Heather Gallivan, associate professor of mathematics education at UNI; Lydia Butters, a former math education student at UNI who now teaches at Cedar Falls (Iowa) High School; and Stephen Nathan Mercil, a former mathematics doctoral student at Iowa State who is now an instructor at the University of St. Thomas, Minnesota.
Teaching data science by starting with what teachers already know
The curriculum, which is a five-week, self-contained module delivered within coursework taken by pre-service math teachers at Iowa State and UNI, focuses on the relationship between math and data science.
"We want to show pre-service math teachers that data science isn't a separate universe from the math they already study," Weber said. "It's built on it."
Many data-science ideas, including modeling, optimization and visualization, grow directly out of algebra, geometry and calculus, so instead of focusing on coding or software, the curriculum module uses familiar mathematical structures to introduce new concepts, Weber said.
A regression line becomes a model.
A classification problem becomes a geometry puzzle.
An optimization routine becomes a function‑minimizing exercise.
Weber said this strategy helps pre-service teachers get past the intimidation factor.
"If we can break down the initial barrier of, 'I don't know what data science is,' then their ability to make that transition becomes pretty quick," he said.
A project shaped by timing and a growing need
The idea for this project began in 2019, when Weber and Mercil first piloted the curriculum at Iowa State. The first full run happened in spring 2020, just as the pandemic forced classes online, Weber said.
The project expanded after Weber teamed up with Gallivan, whose background in statistics helped merge the two universities' approaches. Funding from the Iowa Space Grant Consortium allowed the team to refine the lessons and offer the curriculum at both campuses starting in 2023.
"The module has been taught every spring at Iowa State and UNI since then, and each year, we add improvements based on student feedback and classroom experience," said Weber, who is also a member of a committee assembled by the Iowa Department of Education to help write data science learning standards for the state.
To help future teachers see how data science works in practice, the curriculum uses a mix of synthetic and real‑world datasets.
One set simulates animal‑tracking data — timestamps, locations and headings — to give students a chance to explore visualization, dimensionality reduction and prediction. Another uses housing data collected by local high‑school students, allowing pre‑service teachers to practice multiple regression and think about how they might guide their own students through similar projects.
These examples, Weber and team said, help teachers understand how data‑science questions emerge from the data itself — and not from a prewritten hypothesis.
Preparing teachers for an AI-driven world
Weber said a broader goal of the project is to prepare teachers for classrooms where artificial intelligence and automated decision‑making are already part of students' daily lives, and to help future teachers understand the relationship between AI and data science ("they're closely related," Weber said, "but they aren't the same thing.").
"Data science is the bigger field," Weber said. "It's about using math, statistics and computer tools to make sense of data and find patterns."
Artificial intelligence, he explained, is about creating systems that can do tasks that usually require human thinking. AI systems learn from data, so they depend heavily on the work data science does.
The link between data science and AI comes from machine learning, a part of AI that learns patterns directly from data.
"Machine learning uses the same math and statistics that data science uses," Weber said. "Simply put, data science helps us understand what the data is saying, and AI uses that understanding to make decisions or take action."
The U.S. Bureau of Labor Statistics projects data science jobs will grow 34 percent between 2024 and 2034, a rate that is significantly faster than the average for all occupations.
"Artificial intelligence is powerful, but we'll still need data scientists — humans in the loop," Weber said. "AI systems don't 'think' the way humans do; they learn patterns from large amounts of data and make predictions based on probability. Without someone who understands how that data was collected, what it represents and where it might be misleading, the results can be wrong or even harmful. Data scientists can interpret and contextualize the output of those systems."
Early results show promise
The researchers' curriculum has now run for four consecutive spring semesters at Iowa State and UNI, Weber said, adding that one former student is already teaching data science at a high school.
Additionally, a pre- and post-assessment administered during the first implementation showed measurable gains in students' understanding of data science concepts, suggesting the approach is helping future teachers build both confidence and competence.
Weber said these early signs reinforce the need for continued investment in teacher preparation.
"We hope to obtain additional funding that will help us expand our work and support teachers who are already working in the field with in-service programming and classes that could earn teaching licensure renewal credits," Weber said.
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Read the paper: Weber, E., Gallivan, H., Butters, L., & Nathan Mercil, S. " Leveraging Mathematical Knowledge to Prepare Future Math Teachers to Teach Data Science ," Scatterplot, 3(1). Published online April 2026.
