  The growing role of data science (DS) and machine learning (ML) in
high-energy physics (HEP) is well established and pertinent given the complex
detectors, large data, sets and sophisticated analyses at the heart of HEP
research. Moreover, exploiting symmetries inherent in physics data have
inspired physics-informed ML as a vibrant sub-field of computer science
research. HEP researchers benefit greatly from materials widely available
materials for use in education, training and workforce development. They are
also contributing to these materials and providing software to DS/ML-related
fields. Increasingly, physics departments are offering courses at the
intersection of DS, ML and physics, often using curricula developed by HEP
researchers and involving open software and data used in HEP. In this white
paper, we explore synergies between HEP research and DS/ML education, discuss
opportunities and challenges at this intersection, and propose community
activities that will be mutually beneficial.
