Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice
This addresses the problem of insufficient data science skills among physics educators and students, though it is incremental as it builds on existing community-based approaches without introducing new methods.
The paper tackled the gap in data science training for physics educators by establishing the Data Science Education Community of Practice (DSECOP), which facilitated sharing of best practices and lessons for integrating data science into undergraduate physics curricula, aiming to prepare students for data-driven challenges.
It is becoming increasingly important that physics educators equip their students with the skills to work with data effectively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To address this gap, we created the Data Science Education Community of Practice (DSECOP), bringing together graduate students and physics educators from different institutions and backgrounds to share best practices and lessons learned from integrating data science into undergraduate physics education. In this article we present insights and experiences from this community of practice, highlighting key strategies and challenges in incorporating data science into the introductory physics curriculum. Our goal is to provide guidance and inspiration to educators who seek to integrate data science into their teaching, helping to prepare the next generation of physicists for a data-driven world.