New Inter-Institutional
Art and Science Offering

Application required to join course

"In today's increasingly data-driven world, artists and designers have much to contribute to innovation alongside scientists and engineers," says SAIC President Walter E. Massey. "The complexity and scale of the issues presented by visualizing information in the age of big data require a creativity of approach and mindset in both research and problem-solving. Only by combining the interpretive powers of artists and scientists can we continue to achieve the kinds of breakthroughs necessary to sustain an innovative society and economy."

Data Viz Collaborative is a fall 2014 class that will be team taught by a group of interdisciplinary faculty based at both the School of the Art Institute of Chicago (SAIC) and Northwestern University (NU). The course will enroll ten students from SAIC and ten from NU. Christopher Baker (ATS) and Jessica Westbrook (CP) are the appointed SAIC faculty in the course.

Entry into the class is competitive and a SlideRoom application is required prior March 28. The course is open to both undergraduate and graduate students. Applications will be judged based on the merits of (1) portfolio and (2) rationale for taking the class.

Data Viz Collaborative has two primary goals: (1) to establish a critical dialogue about information visualization across multiple disciplines and (2) to engage students in collaborative research on information visualization using existing data sets. The first goal will be accomplished by engaging students in a series of short lectures delivered by both the science and studio faculty that discuss how images that picture complex data sets help move their own research projects forward or how images might enhance/problematize/critique/promote new knowledge acquisition in science, art, and/or design. The second goal is addressed by inviting students to join one of two research teams, which meet weekly to collectively work on a large data set to experiment with translation of numeric information into various forms. The course will culminate in an exhibition of each team's visualization work.

Testimonials from the Summer 2013 dataviz course:


Summer 2013 Exhibition: Data Viz Collaborative

Read a brief summary of the course and student projects here.

Twenty-one students and nine faculty members from Northwestern University (NU) and the School of the Art Institute of Chicago (SAIC) combined big data with collaborative research, studio arts, and visual communication design last summer on SAIC's downtown campus. The results—creative approaches to information visualization developed in an intensive new course, called Data Viz Collaborative—were on view for three weeks in a free exhibition in the LeRoy Neiman Center Gallery. The course and exhibition, which also traveled to NU, are the latest developments at SAIC in a long history of exploring the connections between art, science, and their processes of discovery.

Participating NU students include Beau Becker, Sara Clifton (fellow), Karna Gowda, Daniel Ha, Felix Hu, Shiqiang Li, Audrey Lustig (fellow), Nick Timkovich (fellow), Rachel Weathered, and Kyle Yakal-Kremski.

Participating SAIC students include Brendan Albano, Will Becker, Richard Blackwell, Sarah Faulk, Yuehao Jiang, Zac Kile, Emma Peng, Miguel Perez (Teaching Assistant), Zeke Raney, Ariel Zekelman, and Shuting Zheng.


Data Viz Collaborative

VISCOM 4560/ATS 4560
Fall 2014: August 27–December 15

Photo: Greg Reigh

Data Viz Collaborative is a new class that will be team taught in the fall of 2014 by a group of interdisciplinary faculty based at both the School of the Art Institute of Chicago (SAIC) and Northwestern University (NU). The course has two primary goals:

  1. Establish a critical dialogue about information visualization across multiple disciplines
    This goal will be accomplished by engaging students in a series of short lectures delivered by both the science and studio faculty that discuss how images that picture complex data sets help move their own research projects forward or how images might enhance/problematize/critique/promote new knowledge acquisition in science, art, and/or design.
  2. Engage students in collaborative research on information visualization using existing data sets
    This goal is addressed by inviting students to join one of three research teams, which meet weekly to collectively work on a large data set to experiment with translation of numeric information into various forms.

The course is open to both undergraduate and graduate students from either institution, and it will meet for the fall semester and culminate in a group exhibition.

Photo by Gregory Reigh.