The Major Studio courses are an opportunity for students to reflect upon the knowledge they have gained in their introductory classes and apply it to the design of a single data visualization project. The course will give students the detailed knowledge that comes from working on a project in depth through the process of concept development, design iteration, implementation, refinement, and critique. Communication of the design’s disciplinary relevance or social value will also be of key importance.

Students will propose the topic for their project—with emphasis placed on topics that are of personal or professional interest. Example domains may include: personal data (i.e., quantified self), geographic mapping, crowdsourcing, medical, civic, financial, political, or business data. Students will be encouraged to reach out to the wide network of resources available at The New School and in the New York City area.

Major Studio 2 culminates in a master’s thesis, consisting of a visual and written component, whereas Major Studio 1 follows a similar arc but is primarily concerned with the quality of the visualization itself. The course iterates through different forms of knowledge production, structured around a sequence of workshops focused on:

  1. concept development
  2. design development
  3. design refinement, and
  4. the final presentation

Tuesday meetings will be concerned with conceptual, visual, and process-oriented aspects of the work. Students will complete a series of readings, deliver research presentations, and make stepwise progress on their projects through writing, sketching, and coding assignments followed by group discussions and individual critique.

Thursdays will be devoted to technical workshops that will provide a grounding in the tools and concepts necessary to bring individual concepts to fruition. Making use of TA tutoring hours is strongly encouraged to provide additional project assistance.


Concept Development

22 January–14 February

Students should come to the first workshop with initial ideas for the domain of their project. Through guided exercises in the workshop, students will develop a project proposal that includes how they will get the data to be visualized, who the intended audience will be, sample use cases for the visualization, and initial design sketches. As part of the workshop, students will also consider their underlying “hypothesis” as well as the explanatory value of their proposed design. The workshop will end with group discussion and critique of the design proposals.

Final deliverables: Statement of research question, identification of data sources, wireframe-level plan for front-end.

Design Development

19 February–14 March

In this workshop students will work to resolve design and technical challenges facing their individual projects. Students will bring to class the data set they will be using, initial efforts to parse the data, refined design sketches/wireframes, and research showing how their work relates to other historical and contemporary data visualizations. In coordination with the instructor students will work hands-on to advance their designs. Through group discussion and critique students will develop a plan for advancing and implementing their design for the remainder of the course.

Final deliverables: Final data, static visualizations prototyping the interactive piece to come, conducted at least one interview with a domain expert, completed introductory text to thesis.

Design Refinement

26 Mar–11 Apr

In this workshop, students will present the in-progress design of their projects as well as their initial insights from working with the data, how they have advanced the design, and any current design or technical challenges. Through directed activities, group discussion, and critique students will develop a plan to complete their project and create an outline for their final project presentation and documentation.

Final deliverables:
final mockups of front-end, full spec for final project and development timeline.

Final Presentation

16 Apr–9 May

Following an initial review and incorporation of feedback of their projects, students will work to refine and finalize their design and presentations. Final presentations of each project are to highlight the value of the data visualization and how it can be used as well as key discoveries relating to the problem domain, the data, and the overall process of designing and implementing the visualization. Group discussion and critique of the design solutions will highlight best practices and future opportunities.

Final deliverables: written thesis (due 9 May), and slideshow-supported presentation (date t.b.d.).

Required reading

  • Booth, Wayne C., and Gregory G. Colomb. The Craft of Research. 3rd ed. Chicago: University of Chicago Press, 2008.

Recommended readings

  • Friendly, Michael. A brief history of data visualization. Handbook of data visualization (2008): 15-56.
  • Benjamin, Walter. The Work of Art in the Age of Mechanical Reproduction, and other writings on media.
  • Reas, Casey, et al. What Is Code? Form+Code in Design, Art, and Architecture, Princeton Architectural Press, 2010.
  • Baudrillard, Jean. The system of collecting. The cultures of collecting 18 (1994).
  • Tufte, Edward R. Graphical excellence. Analysis 17 (1973): 21.
  • McCloud, Scott. Understanding comics: The invisible art. Northampton, Mass (1993).
  • Steyerl, Hito. In defense of the poor image. e-flux journal 10.11 (2009)
  • Fernanda Viégas,Martin Wattenberg. Design and Redesign in Data Visualization. Malofiej 22, Annual Book.

Materials and Supplies

  • Personal Github repository
  • Cloud services and web hosting as needed

Learning Outcomes

Through the successful completion of this course, students will be able to:

  1. Demonstrate advanced knowledge of, and be able to critically analyze, content, form, dynamics and interactivity as it applies to data visualization.
  2. Demonstrate the ability to develop a design proposal based on a design hypothesis and user needs.
  3. Conceptualize and implement a significant data visualization project.
  4. Demonstrate a deeper understanding of how to find, access, and filter relevant data sources.
  5. Apply iterative design cycles to refine a design concept.
  6. Demonstrate the ability to collaborate effectively as part of a design team.
  7. Demonstrate the ability to create visualizations using web-based programming libraries.
  8. Understand and communicate the value that effective data visualization can play in a business or social context.
  9. Use a sophisticated vocabulary of visual design and social responsibility to analyze, critique, and frame constructive conversations about contemporary data visualization design work.
  10. Be able to give and respond to critique productively.

Assessable Tasks


Quantity in and of itself is not a marker of quality or growth. Students are expected to be producing work of high caliber. The work should articulate intended ideas and concepts and demonstrate an independence of thought and be original in nature. The execution of the work should effectively employ technical, formal, and/or conceptual strategies that effectively work together to communicate the intended meaning of the work.

Critiques, presentations, and meetings

Students are expected to possess a knowledge and understanding of their own work and the issues surrounding it and be able to articulate them. Participants are expected to be well prepared for all presentations and meetings. A lack of attendance and considered preparation overall will impact the final grade. As a contributing member of a collaborative academic group, students are expected to evaluate the work of other participants and express critique in a professional, constructive manner.

Assignments and projects

Thorough and on-time completion of all assignments is essential. Failure to meet deadlines, late or incomplete assignments will dramatically reduce your grade. Repeated or chronic lateness or incomplete assignments will result in a failing grade for the course.


Grading is based on careful consideration all factors listed above. Please be aware that unexcused and/or excessive absence from class will also impact your grade. As a graduate student you are required to maintain a 3.0 GPA to remain in good standing.

Final Grade Calculation

Class Participation: 10%
Readings and Presentations: 10%
7 Figures in 7 Days: 10%
Final Project (writing): 20%
Final Project (design): 50%

Consult the Grading & Class Policies page for additional details.

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