Short Description

Effective oral and written communication, across diverse target audiences, to facilitate understanding and decision-making. How to present and interpret data, with productive skepticism and an awareness of assumptions and bias.

Learning Outcomes

By the end of the course, students are expected to be able to:

  1. Outline the components of a good scientific argument, paying attention to claims, reasons, evidence, assumptions, bias, validity, reliability, etc.
  2. Identify the components of a good experiment or data collection effort, paying attention to how the data was collected and how it is being used to construct a scientific model; identify limitations of the data and model.
  3. Work effectively with teams and domain experts on data science problems.
  4. Communicate uncertainty to diverse audiences.
  5. Explain the purpose and strengths of consistent documentation practices.
  6. Write effectively on technical data science topics for a nontechinal audience.
  7. Present data science results to diverse audiences and recommend subsequent action to decision makers.
  8. Communicate effectively through oral presentations and written reports. Distinguish between the goals of each.

Reference Material

  • Booth, Wayne; Colomb, Gregory; and Williams, Joseph. The Craft of Research, 3rd Edition, Chicago Guides to Writing, Editing, and Publishing, University of Chicago Press, 2008. (also available as a free download)
  • Aaron, Jane and Morrison, Aimee. The Little, Brown Compact Handbook, 5th Canadian Edition, Pearson, 2012.
  • Messenger, William E.; de Bruyn, Jan; Brown, Judy; and Montagnes, Ramona. The Canadian Writer’s Handbook, 6th Edition, Oxford University Press, 2014,
  • Reynolds, Garr. Presentation Zen: Simple Ideas on Presentation Design and Delivery, 2nd Edition, New Riders, 2011.
  • Zelazny, Gene. Say It with Charts, 4th Edtition, McGraw-Hill, 2001.

Instructor (2016-2017)

Note: information on this page is preliminary and subject to change.