Competence with geospatial data science involves a mixture of knowledge and skills in analytics, computing, and the spatial sciences. The ability to communicate effectively about inherent uncertainty and ethical considerations, often via data visualizations, is also part of the workflow.  In designing learning experiences for students to practice these workflows, what approaches are possible and which work better in different learning environments? How can instructors use messy real-world data, case studies, and classroom, laboratory or field work opportunities to increase the likelihood that the knowledge and skills will be transferable across contexts and disciplines?  Join us as we discuss these and related topics.