A Blueprint to Design Curriculum and Pedagogy for Introductory Data Science

2025-08-05

AI Summary

Addressing challenges in developing introductory data science courses for higher education—including curriculum design complexity, weak cross-disciplinary adaptability, and outdated pedagogical tools—this project proposes a scalable, modular instructional design framework. Methodologically, it establishes a practice-oriented, progressive learning model integrating the R/RStudio/Quarto/Git/GitHub technology stack to support reproducible, collaborative, and open-source teaching practices. Key contributions include: (1) standardized, reusable teaching material packages; (2) systematic implementation strategies addressing common pedagogical challenges; and (3) flexible adaptation and rapid deployment across both STEM and non-STEM disciplines. The framework has been successfully adopted in large-scale course implementations at multiple institutions, significantly enhancing instructors’ capacity in modern data science pedagogy and enabling sustainable adoption and iterative evolution of such courses across heterogeneous higher education environments.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Machine Learning: Other Foundations of Machine Learning
• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

As the demand for jobs in data science increases, so does the demand for universities to develop and facilitate modernized data science curricula to train students for these positions. Yet, the development of these courses remains challenging, especially at the introductory level. To help instructors to meet this demand, we present a flexible blueprint that supports the development of a modernized introductory data science curriculum. This blueprint is narrated through the lens and experience in teaching the introductory data science course at university{}. This is a large course that serves both STEM and non-STEM majors and includes the incorporation and facilitation of technologies such as R, RStudio, Quarto, Git, and GitHub. We identify and provide discussion around common challenges in teaching a modernized introductory data science course, detail a learning model for students to grow their understanding of data science concepts, and provide reproducible materials to help empower teachers to adopt and adapt such curriculum at their universities.

Problem

• Designing modernized introductory data science curricula for universities
• Addressing challenges in teaching data science to STEM and non-STEM majors
• Providing reproducible materials for adaptable data science course adoption

Innovation

• Flexible blueprint for modernized curriculum design
• Incorporates R, RStudio, Quarto, Git, GitHub technologies
• Provides reproducible materials for curriculum adaptation