Spring 2024 - CMNS 313 D100

Topics in Data and Society (4)

Visual Culture & Data Visualization

Class Number: 3008

Delivery Method: In Person


  • Course Times + Location:

    Jan 8 – Apr 12, 2024: Tue, 10:30 a.m.–12:20 p.m.

  • Prerequisites:

    17 CMNS units with a minimum grade of C- or 45 units with a minimum CGPA of 2.00.



Topics in the social, political, and cultural aspects of data and datafication. Explores social and philosophical implications of gathering, interpreting, and managing data. Topics include: data protection, visualization or sonification, data activism, big data, algorithmic bias and decision making, AI harms, big data, and the political economy of data. This course can be repeated once for credit (up to a maximum of two times).


The availability of powerful and relatively inexpensive digital technologies for documenting and analyzing audio-visual phenomena has made visualization design an important component of communications strategies in many areas. New employment opportunities in areas such as ‘data journalism’, ‘infographics’, and ‘visual analytics’ have emerged as potential career paths for students in the field of communication.

Students will engage with core concepts in theories and methods used in visual communications and to develop skills in visualization design. Visualization design is not merely a set of techniques for organizing and presenting data. Visualization techniques have the potential to offer insights into phenomena and patterns that we cannot necessarily ‘see’ with the naked eye. Visual representations are not always neutral presentations of ‘facts’ but have the potential to misinform and misrepresent. The ability to critically analyze the visual display of data as information and to design effective strategies in visual communications is of fundamental importance for understanding and participating in contemporary society.

Visual culture and data visualization processes thus conceived are powerful ways of knowing and engaging with the world. This course investigates both theories and techniques of visualization design. Themes covered in the course include: theories of visualization in relation to communication studies; methodologies for developing data visualization that are appropriate for specific content and contexts; analysis of imaging conventions and innovative directions in the visual display of quantitative and qualitative information; and factors that need to be considered in the design of visual communication in diverse cultural contexts and in global communication networks.


  • Learn how to present data in an understandable, efficient, effective, and aesthetic manner, for the purposes of explaining ideas and analyzing data.
  • Acquire skills at designing and evaluating data visualizations and other forms of visual presentation.
  • Equip students with a critical, historically grounded understanding of visual culture and how it applies to data visualization.


  • First Visualization Design Project 10%
  • Second Visualization Design Project 20%
  • Third Visualization Design Project 15%
  • Final Applied Design Project 30%
  • Lecture and Tutorial Participation* 25%


Students with credit for CMNS 325 should not take this course for further credit.

Note: This course does not have a final exam during the exam period.

*Attendance at all scheduled tutorial sessions is mandatory. You are also required to either attend lectures or view all lecture materials once posted on Canvas. Marks will be deducted for absences except in the case of valid documented absences acceptable to the Registrar. (A completed SFU healthcare provider form is required for absences due to healthcare issues.)

Lab and tutorial participation grades will take into account preparation, for example, bringing the results of take-home exercises to class, and demonstrating in discussions and Canvas postings that you have done the work assigned and completed reading assignments on time. No grades will be given for late submission of exercises and major assignments.

Details on the major assignments will be presented in separate handouts. 

The school expects that the grades awarded in this course will bear some reasonable relation to established university-wide practices with respect to both levels and distribution of grades. In addition, the School will follow Policy S10.01 with respect to Academic Integrity, and Policies S10.02, S10.03 and S10.04 as regards Student Discipline (note: as of May 1, 2009 the previous T10 series of policies covering Intellectual Honesty (T10.02) and Academic Discipline (T10.03) have been replaced with the new S10 series of policies). For further information see: www.sfu.ca/policies/Students/index.html.




Kennedy, H., & Engebretsen, M. (Eds.). (2020). Data Visualization in Society. Amsterdam University Press; https://www.aup.nl/en/book/9789048543137/data-visualization-in-society

All other required readings will be provided through Canvas.  Additional materials required for seminar work will be provided in class.


Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Registrar Notes:


SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html