Summer 2024 - CMNS 353 D100

Topics in Science, Technology and Society (4)

Data Fluencies

Class Number: 1126

Delivery Method: In Person


  • Course Times + Location:

    May 6 – Aug 2, 2024: Mon, 2:30–5:20 p.m.

  • Prerequisites:

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



Examination of the emergence and shaping of information and communication technologies and science in the digital age. Explores new media and social change between everyday life, social institutions, and various enterprises. Emphasis is placed on social context and relations of power. This course can be repeated once for credit if second topic is different (up to a maximum of two times).


Topic for Summer 2024:  Data Fluencies

What is data? How is data generated? Classified? Cleaned? Stored? “Cooked?” What are machines “learning” from data? What do machines “predict” from data? Why does it matter for studies of media and communication?

This course answers these questions by exploring the possibilities and limitations of a data-driven world. We will unpack key concepts related to data in class and labs concerning their history, current applications, and implications for communication studies. The students will explore a combination of primary texts and theoretical approaches that discuss data capture, storage, analysis, prediction, and their entanglements with knowledge formation and power in society. The students will also be introduced to case studies with hands-on data science labs to explore the theory learned in class.

Note that this course will run in two physical settings: the first 1.5-2 hours will be in a regular classroom, and the remainder will be in the computer lab where students will engage with data science tools. Students will use Google Colaboratory to program in Python and run data analysis (no previous experience required).


* Understand how theories related to data, computing histories, machine learning, and power relate to issues in contemporary communication technologies.

* Introduce students to relevant terminology and basics of data analysis and data science.

* Explore valuable tools to take on the ethical challenges of dealing with data in industry and academia.

* Develop critical and creative thinking to reimagine techno-futures in a data-filled world


  • Project - Proposal 5%
  • Project - Presentation 10%
  • Project - Final Submission 20%
  • Weekly Reading Responses 20%
  • Data Analysis Exercises 15%
  • Short Essay 15%
  • Participation (Seminar + Lab) 15%


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:



All required readings will be made available on Canvas, and lab materials will be shared on Google Colab.

Registrar Notes:


SFU’s Academic Integrity website 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.