AMES, Iowa — In a transformative approach to teacher education, Eric Weber, professor and chair of mathematics at Iowa State University, is reimagining how future math educators can be prepared to teach data science—a field rapidly becoming indispensable in the 21st century. Instead of diving straight into coding or algorithms, Weber encourages pre-service teachers to view data science through a conceptual lens akin to running the scientific method in reverse. This paradigm shift is reshaping the very foundations of math teacher preparation.
“We begin by considering the data itself before any hypotheses are drawn,” Weber explains. “Data science starts with existing datasets—possibly gathered years ago or intended for purposes entirely unrelated to the current question. From there, we search for patterns, correlations, or anomalies that point us toward meaningful questions to investigate.” This approach contrasts sharply with traditional scientific inquiry, which typically commences with hypothesis formulation followed by data collection to test predictions.
This methodological inversion forms the cornerstone of a carefully designed curriculum that Weber and colleagues developed in collaboration with faculty at Iowa State and the University of Northern Iowa (UNI). Their comprehensive five-week module integrates seamlessly into existing math education courses for pre-service teachers, emphasizing that data science extends naturally from the mathematics core they already master. This curriculum underscores the intimate relationship between classical mathematical disciplines and data science’s practical tools and challenges, aiming to demystify the latter for educators-in-training.
The academic community’s recognition of data science as an essential high school subject is growing robustly, reinforced by endorsements from prominent mathematics and statistics societies. Yet a critical gap persists: many high school teachers tasked with delivering data science curricula lack targeted preparation in the field. Weber and his team argue that empowering future math teachers with foundational knowledge of data science principles and practices will enable them to fill this educational void effectively.
Rather than emphasizing software skills or programming languages, the module links familiar mathematical concepts to data science applications. For instance, regression analysis is framed as modeling, classification problems are conceptualized as geometric puzzles, and optimization challenges are interpreted as exercises rooted in function minimization. This pedagogical strategy reduces intimidation and builds confidence by connecting new information to well-understood mathematical forms.
The initiative traces its origins to a 2019 pilot at Iowa State, just before the COVID-19 pandemic necessitated a swift transition to virtual classrooms. This initial version evolved through ongoing collaboration and refinement, aided by funding from the Iowa Space Grant Consortium. Since 2023, the curriculum has been taught at both Iowa State and UNI each spring, incorporating iterative improvements based on student feedback and instructional experience, making it a living, adaptable educational innovation.
To illustrate data science in action, the team employs both synthetic and real-world datasets. One notable example is an animal-tracking dataset containing timestamps, geographic positions, and directional headings, which serves as a platform for exploring advanced topics like data visualization, dimensionality reduction, and predictive modeling. Another dataset derived from housing data collected by local high school students allows pre-service teachers to rehearse regression techniques and consider how they might scaffold similar projects in their future classrooms.
As artificial intelligence (AI) systems permeate daily life, preparing teachers to understand and convey the nuanced relationship between data science and AI becomes imperative. Weber stresses that while these fields intersect—particularly through machine learning—data science encompasses a broader array of mathematical, statistical, and computational methods aimed at extracting knowledge from data. AI, conversely, focuses on constructing systems that replicate aspects of human cognition.
“The mathematical backbone of machine learning algorithms is deeply rooted in traditional data science tools,” Weber notes. “Data science helps interpret and understand data, while AI leverages this understanding to perform autonomous tasks and decision-making. This distinction is critical for educators to communicate clearly to students navigating an increasingly AI-driven world.”
Market trends affirm the urgency of developing the next generation of data science educators. The U.S. Bureau of Labor Statistics forecasts a remarkable 34% growth rate in data science jobs from 2024 to 2034, outpacing most other occupations. Despite AI’s growing prominence, human insight remains vital. Weber warns, “AI algorithms don’t reason as humans do; they rely on large datasets and statistical probabilities. Without proper human oversight to contextualize data collection methods and potential biases, AI outputs can be misleading or even harmful.”
Preliminary assessments of the curriculum’s impact are promising. After four consecutive spring semesters, early data indicate meaningful gains in pre-service teachers’ understanding of core data science concepts and increased confidence to teach these subjects. One alumna from the program now actively teaches data science at the high school level, exemplifying the curriculum’s real-world efficacy and potential for broader educational influence.
Looking forward, Weber emphasizes the need for sustained investment and expansion efforts. His team aims to secure additional funding that would enable not only program scaling but also professional development opportunities targeting in-service teachers. Such offerings may include refresher courses, workshops, or classes that fulfill licensure renewal requirements, addressing the urgent need for continual upskilling in the dynamic landscape of math education.
At its heart, this initiative underscores a vital pedagogical principle: data science education should not be viewed as an isolated domain but rather as an extension of mathematical knowledge already embedded in classroom teaching. By aligning data science with the mathematical frameworks familiar to educators, Weber’s curriculum is dismantling barriers and equipping future teachers to confidently usher data literacy into every high school syllabus.
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Subject of Research: Preparing future mathematics teachers to effectively teach data science concepts through a targeted curriculum that integrates data science with existing mathematical disciplines.
Article Title: Leveraging Mathematical Knowledge to Prepare Future Math Teachers to Teach Data Science
News Publication Date: April 8, 2026
Web References:
- https://educate.iowa.gov/boards/computer-science-data-science-artificial-intelligence-standards-revision-review-teams
- https://www.bls.gov/ooh/math/data-scientists.htm
- https://doi.org/10.1080/29932955.2026.2644686
References:
Weber, E., Gallivan, H., Butters, L., & Nathan Mercil, S. (2026). Leveraging Mathematical Knowledge to Prepare Future Math Teachers to Teach Data Science. Scatterplot, 3(1). https://doi.org/10.1080/29932955.2026.2644686
Image Credits: Photo illustration by Deb Berger/Iowa State University.
Keywords: Data Science Education, Mathematics Teacher Preparation, Curriculum Development, Pre-service Teachers, Machine Learning, Artificial Intelligence, Data Literacy, STEM Education, Mathematics Integration, Educational Innovation
