The biological and environmental sciences have been rapidly and fundamentally reshaped by recent technological advances, including increased computational power, sensor technologies, publicly available software and data, and Internet connectivity. These advances, together with the demands that we provide our students with technical skills to navigate data and technology in the 21st century, necessitate the integration of computational data sciences into our undergraduate and graduate classrooms. However, many instructors do not feel qualified or prepared to teach such materials, limiting the usefulness of already developed computational course or lab modules. This “train the teachers” workshop will include both technical training for fundamental data science skills, including R, Github, and Markdown, as well as pedagogical training for communicating those skills in the classroom and lab. Working through data science examples in-real time, participants will experience the material as a learner and gain strategies toward including and being able to teach these skills in their own courses. While our focus will primarily be on the undergraduate classroom, content will likely be relevant for many mixed-level or graduate classrooms as well. The goal is to equip instructors with technical tools and instructional strategies that they can then confidently customize to their own curricular goals and institutional needs.


  • Dr. Sarah R. Supp
  • Dr. Andrew J. Kerkhoff
  • Dr. Matthew E. Aiello-Lammens
  • Dr. Naupaka Zimmerman


  • Date: Sunday, August 11, 2019
  • Time: 8:00 AM to 5:00 PM (there will be a 1-hour lunch break)
  • Place: Kentucky International Convention Center - Room L007


Times are approximate.

Settle in and meet one another (8:00 AM to 8:30 AM)

  • Participant introductions

Introduction to the workshop

Data science for undergraduate biology and environmental science (9:00 AM to 9:30 AM)

Methods and tools for teaching data science in your classroom (9:30 AM to 12:00 Noon)

Lunch Break (12:00 Noon to 1:00 PM)

Development of data science lessons (1:00 PM to 2:30 PM)

Hands-on lesson development (2:30 PM to 4:30 PM)

  • Start developing a lesson of your own
  • Individually or in small groups - use the tools you want to practice

Wrap-up (4:30 PM to 5:00 PM)

Additional Resources