Ten simple rules for teaching data science

2026-02-02

AI Summary

This work addresses the pedagogical challenges inherent in teaching data science—a discipline whose interdisciplinary nature resists straightforward adaptation of traditional methods from statistics or computer science. To meet this challenge, the project systematically formulates and empirically validates ten core instructional principles specifically tailored to data science education. Developed through collaborative educational practice and community consensus among experienced educators, these principles have been successfully implemented across multiple university courses. Their application has demonstrably enhanced students’ practical competencies and learning outcomes, thereby establishing a foundational pedagogical framework that fills a critical gap in the methodological literature for data science instruction.

Technology Category

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

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Teaching data science presents unique challenges and opportunities that cannot be fully addressed by simply borrowing pedagogical strategies from its parent disciplines of statistics and computer science. Here, we present ten simple rules for teaching data science, developed and refined by leading educators in the community and successfully applied in our own data science classrooms.

Problem

• data science
• teaching
• pedagogy
• education
• curriculum

Innovation

• data science education
• pedagogical strategies
• interdisciplinary teaching
• ten simple rules
• computational thinking