Given the growing demand for data science skills, many physics programs are beginning to incorporate data science into their curriculums. A team of physicists led by the Topical Group on Data Science (GDS) and funded by the American Physical Society (APS) is therefore developing small teaching modules for data science that can be easily integrated into undergraduate physics courses. The project, known as the Data Science Education Community of Practice (DSECOP), was designed to bridge the gap between traditional physics education and the skills physicists need in industry. Our PhD student Karan Shah has been accepted into the program for the second time!
Karan, great to hear that you have been selected the second year in a row! After all, this is a limited program that attracts many applicants. Why did you want to be part of the program again?
Shah: Thank you once again! Last year, I had an amazing experience participating in the program and thoroughly enjoyed my time there as a DSECOP Fellow. I gained valuable insights into designing educational materials, specifically addressing the challenges that both instructors and students encounter while incorporating data science into physics course materials. The process of breaking down complex concepts into digestible modules also enhanced my understanding of the subjects. The program’s diverse range of topics, the collaborative atmosphere, and the opportunity to engage with like-minded individuals made it an exceptionally attractive experience for me.
And why do you think your application did convince the reviewers also this time?
Shah: Although I can’t be certain, I believe my background in physics and machine learning, combined with the enthusiasm and dedication I demonstrated during my first experience with the program, played a role in strengthening my application.
What is actually happening within the program? Is the procedure the same as last year?
Shah: While the overall schedule of the program remains similar to last year, there have been a few notable improvements based on the lessons learned from our previous experience. This year, the program has been structured more effectively, with biweekly meetings where fellows have the opportunity to showcase their works in progress. In June 2023, we will present our first modules at the DSECOP Workshop, which will take place at the University of Maryland in the US. This year we are also focusing more on soliciting feedback from faculty members, who would actually use the modules, at various stages of the program.
Last year you said you wanted to make the teaching material that is being developed also available for people outside the US. Can you provide us a download link and tell us if you managed to teach students at CASUS already?
Shah: Absolutely! All the material we developed in the DSECOP is fully open-source, and we’ve made it accessible through a GitHub repository. Instructors can find the material and reach out to us for support using the following link. In fact, I’ve already had the opportunity to present the material from my module in an invited lecture for the “Many-body theory” (Vielteilchentheorie) seminar at Kiel University. Additionally, some of the introductory material was utilized in teaching high school interns about neural networks here at CASUS. I would also like to add that we were recently featured in an American Physical Society news article.