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.
By the end of the course, students are expected to be able to:
- Outline the components of a good scientific argument, paying attention to claims, reasons, evidence, assumptions, bias, validity, reliability, etc.
- 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.
- Work effectively with teams and domain experts on data science problems.
- Communicate uncertainty to diverse audiences.
- Explain the purpose and strengths of consistent documentation practices.
- Write effectively on technical data science topics for a nontechinal audience.
- Present data science results to diverse audiences and recommend subsequent action to decision makers.
- Communicate effectively through oral presentations and written reports. Distinguish between the goals of each.
- 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.
Note: information on this page is preliminary and subject to change.