Seeing Is/And Believing: The Rhetoric of (Big) Data Visualization

Course Description

The Latin etymology of “data” means “something given,” and though we’ve largely forgotten that original definition, it’s helpful to think about data not as facts per se, but as “givens” that can be used to construct a variety of different arguments and conclusions; they act as a rhetorical basis, a premise. Data does not intrinsically imply truth. Yes we can find truth in data, through a process of honest inference. But we can also find and argue multiple truths or even outright falsehoods from data. --Nick Diakopoulus, “The Rhetoric of Data”

In today’s information economy, data is the new oil. This new era of “big data” brings with it ever-prescient concerns as to understanding how (both methods and ethics) we gather, select, interpret, and communicate data as professionals, scientists, and rhetors. Given the eye has the bandwidth of a high speed computer, visualization provides a fast, effective, and highly cognizant means of representing data (McCandless). Studying data visualization helps us understand how (re)emerging genres such as photographs, graphic novels and comic books, infographics, tweets, and chord diagrams serve as economical means of data compression and visualization to combat an information-saturated landscape. In sum, data visualization is a rhetorical strategy predicated on the filters of invention, selection, and arrangement to combat information overload, eliminate data junk, and streamline and highlight patterns and trends across phenomenon.

As the next iteration of visual rhetoric, data visualization has important implications for studies in rhetorical history as much as it does for scientists and professionals looking to communicate more effectively. That is, to seriously engage ideographic and visual modes of communication is to rethink the history of writing writ large; in fact, some have argued this (re)turn to visual rhetoric and literacy is a move towards the decolonization of public language (Mignolo; Baca; Anzaldua; Howes). That is, rhetoric studies has both an a vocational and ethical imperative to understand the rhetoric of data visualization as a salient communication mode of the 21st century.

In this course, students will study and practice techniques and rhetorics of data visualization based on principles of rhetorical history, visual rhetorics and graphic design as well as cognitive science, design thinking, and other disciplines that inform critical conversations around information display and data visualization.

Main Assignments

In addition to class discussion, students will engage in data analysis and visualization projects--from original analyses of data visualization to redesigning bad visualizations (infographics, charts, maps) to original visualizations of data using a variety of digital and communicative tools.

Assignments scaffold students toward a critical engagement with the production and analysis of data visualization and its various tools, media, and rhetorical effects. For example, students might build a language corpus and run a computational linguistic analysis for word count; or they might scrape a database or social media site--findings therein could support various visualizations of emerging patterns in keywords and themes that students use to derive original analysis and interpretation of findings (see, for example, a key-word chord diagram for #RSA2016 Tweets by Chris Lindgren ).

  1. Reading reflections (10%)--due weekly
    • These are weekly write-ups/bog posts that address the course readings on various themes pertaining to data visualization rhetoric: selection, clarity, ethics, diversity, etc. (Students might present these in class to encourage student-directed discussion)
  2. Datavis analysis (30%)-due Wks 2-3
    • Students write three short (2-3pp) papers in the first two weeks to ground them in analytic approaches to “reading” data visualization as text.
      • Graphic novel/comic/photograph (10%)
      • Infographic/map/poster (10%)
      • Student choice (10%)
  3. Datavis redesign (20%)-due Wk6
    • Students pick a “bad/inneffective” data visualization text and redesign it, applying principles of visual design and data visualization supported by readings and activities. Students write a short cover letter(2-3pp) justifying their choices.
  4. Data scrape and visualization report (30%)-due Wk10
    • As the final project, students will produce their own research project that focuses on the generation and visualization of an original data set (see above example #RSA2016 chord diagram). Students then compose a report (6-8pps) on the implications of the findings and its visualization.
    • Final Reflection (10%)--due wk 10
      • Students compose one final data visualization of their learning experiences in PWR91, scraped from their reflective writing and reading reflections, and write a short cover letter (1-2pp) addressing their design choices and seminal learning experiences.