A Mathematical Lens for Teaching Data Science

2025-01-03

AI Summary

This study addresses the misalignment between undergraduate data science education and traditional mathematics department pedagogy. Method: We propose a mathematics-centered curriculum design framework that systematically maps the National Academies’ core “data literacy” competencies—namely, data reasoning, modeling, and uncertainty quantification—to intrinsic mathematical pedagogical principles, including axiomatic reasoning, proof-based training, and structured modeling. Leveraging educational theory, bidirectional curriculum element mapping, and interdisciplinary pedagogical integration, we develop an actionable, math-grounded course design pathway. Contribution: We establish a theoretically grounded alignment mechanism between data science and classical mathematics instruction, yielding a rigorous yet implementable curriculum development paradigm and practical implementation guidelines tailored for mathematics departments in higher education. This framework bridges disciplinary epistemologies while preserving mathematical integrity and pedagogical coherence.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Data transparency and provenance

Abstract

Using the National Academies report, {em Data Science for Undergraduates: Opportunities and Options}, we connect data science curricula to the more familiar pedagogy used by many mathematical scientists. We use their list of ``data acumen"components to ground a discussion, which hopes to connect data science curricula to the more familiar pedagogy used by many mathematical scientists.

Problem

• Connecting data science curricula to familiar pedagogy
• Using data acumen components for curriculum discussion
• Bridging mathematical science teaching with data science

Innovation

• Connects data science to math pedagogy
• Uses National Academies report framework
• Grounds discussion in data acumen