# Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice

arXiv: https://arxiv.org/abs/2403.00961 · HTML: https://mlsanity.com/paper/2403.00961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry](https://mlsanity.com/paper/1806.03417/) (2018)
- [Novelty Detection Meets Collider Physics](https://mlsanity.com/paper/1807.10261/) (2018)
- [Data Science and Machine Learning in Education](https://mlsanity.com/paper/2207.09060/) (2022)
- [Data Science from 1963 to 2012](https://mlsanity.com/paper/2311.03292/) (2023)

## Abstract (arXiv)

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.