Best Practices to Upskill Everyone to Broaden Participation in Data Science

This session aims to share best practices for upskilling students from diverse majors in using data science to address problems within their respective disciplines. A significant challenge in this endeavor is achieving proficiency in programming-based tools, which often act as a barrier to student success. Non-programming tools, while more accessible and user-friendly for beginners, have inherent limitations such as the size of the datasets they can handle, limiting their effectiveness for more complex analyses. This session will explore the best practices for creating a data analytic pathway that transitions from no-code to low-code to high-code tools. The co-chairs will share their experiences in upskilling students from non-computing disciplines, beginning with spreadsheets, CODAP, and Weka, before transitioning to provide a gentle introduction to accessible high-code tools. The session will include audience participation through table discussions and share-outs, focusing on the following prompts:
1. What tools have you used, and what limitations or obstacles have your students encountered with those tools?
2. What resources do you believe are necessary or helpful to enable your students to overcome these obstacles? (Basically, your wish list.)
3. What ideas have you implemented, or do you have, to bring a wider group of students, teachers, and others into data science?
This work is supported through NSF grant #2245958.