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There is a critical need to teach physicists skills beyond the subject material so students are prepared for multiple career paths. Projects, such as the Effective Practices for Physics Programs (EP3) guide and the Phys21 report from the Joint Taskforce on Undergraduate Physics Programs (J-TUPP), indicate this. These efforts coincide with an explosive growth of data science within physics. Data science is rapidly emerging as an excellent career option. However, formal instruction is only starting to be included in physics curricula . Students mostly learn this material on their own—often at the graduate level, or occasionally through capstone projects.
We see an opportunity to quickly boost data science education by leveraging investments in computation, which is becoming normalized in the physics curriculum. The 2016 AAPT Recommendations for Computational Physics in the Undergraduate Physics Curriculum report and the Partnership for Integration of Computation in Undergraduate Physics (PICUP) further galvanized physics educators in seeing the importance of and working towards integrating computation in typical physics courses, rather than a stand alone course.
Although computation is becoming normalized in undergraduate physics education, data science education is not similarly included. The 2016 AAPT report preceded the rapid emergence of resources to teach data science. However, the report acknowledged the importance of this area and advocated that educators pay attention to the developments that may allow for them to teach such skills. In the time since the report, developing data science skills is more accessible through newly available tools.
The Data Science Education Community of Practice (DSECOP), a program funded by the APS Innovation Fund and led by the APS Group on Data Science (GDS), seeks to support physics educators in integrating data science in their courses in multiple ways: