Teaching Data Science

MIDAS Working Group: Teaching Data Science

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The Michigan Institute for Data Science (MIDAS) continues to convene a working group on teaching data science. As we incorporate data science into almost every level of teaching, many issues need to be thoroughly thought out: How do we teach data science to students with various levels of preparation, from those with little quantitative training to STEM students? How do we build data science modules to incorporate into existing domain science courses? How do we raise awareness of ethics and social responsibility in data science teaching? How do we teach data science to independent researchers, including faculty, who want to build data science into their research? What teaching resources are available at UM? Our working group welcomes anyone interested in these topics. We are developing an interdisciplinary team to foster new ideas and collaborations in the development of data science teaching methods and materials.

Please RSVP.  

The agenda for the meeting includes:

  • Introduction
  • Short presentations
    • Kerby Shedden (Professor, Statistics, and CSCAR director) will share insight from his experience teaching “capstone” style courses for undergraduate and MS students, based around case studies and focus on methods, formulating good questions, and writing.
    • Heather Mayes (Assistant Professor, Chemical Engineering) will talk about the design of a Data Science ramp-up course for engineering students and how to integrate it with existing course offerings.
    • Aaron Keys (data scientist, Airbnb) will give the industry perspective on the various training paths that students can take for a career in data science.
  • Open discussion of ideas and collaboration, and sharing resources

For questions, please contact Jing Liu, MIDAS Senior Scientist and Industry Partnership Leader (ljing@umich.edu734-764-2750).