Pre Survey Response Themes

Prior to the workshop, participants were asked about what opportunities and barriers exist to teaching Data Science and/or Environmental Studies in liberal arts colleges. A summary of responses is found below, and will help frame our working group time at the May 31-June 1 workshop.

  • Ben Ho, Vassar, Economics
  • Jonathan Wilson, Haverford, Biology and Environmental Studies
  • Helen White, Haverford, Chemistry & Environmental Studies
  • Brad Johnson, Davidson, Environmental Studies
  • Alberto Lopez, Amherst, Chemistry
  • Steven Miller, Williams, Mathematics
  • Chad Topaz, Williams, Mathematics
  • Fuji Lozada, Davidson, Anthropology & Environmental Studies
  • Jingchen (Monika) Hu, Vassar
  • Ming An, Vassar, Statistics
  • Nick Horton, Amherst, Statistics
  • Guillermo Douglass-Jaimes, Pomona, Environmental Analysis
  • Natalia Toporikova, Washington & Lee, Biology
  • Moataz Khalifa, Washington & Lee, Physics and Engineering
  • Deborah Gross, Carleton, Chemistry
  • Gordon Jones, Hamilton, Physics
  • Trish Ferrett, Carleton, Chemistry
  • Andy Anderson, Amherst, Academic Technology Specialist

Interest in collaborations

Resources for teaching

In particular, sharing:

  • real-world, data driven examples
  • syllabi
  • teaching modules
  • course structures
  • recommended textbooks/websites/tutorials/placement exams/videos/simulations/deatasets/data visualization tools
  • models for teaching with data in liberal arts contexts
  • strategies for teaching with large datasets
  • data science education pedagogy
Developing courses & materials collaboratively. This might help:
  • develop materials in a short time frame
  • create materials that everyone needs
Collaborative teaching
  • co-teaching
  • visiting speakers
  • how to account for added time needed to prepare for these (funding, teaching credit),

Research collaborations

  • find research teams to work on novel datasets
  • experiments across campuses (for bigger n's)
  • math preparation and prerequisites
  • writing about data