Data Science Education in Undergraduate Physics: Lessons Learned from a
  Community of Practice
Karan Shah Julie Butler Alexis Knaub Anıl Zenginoğlu William Ratcliff Mohammad Soltanieh-ha
With the increasing availability of diverse datasets, ranging from
small-scale experimental data points to large and complex data repositories and
powerful data analysis tools, it is 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 in 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.