HIST / STS / SOC 2604, Fall 2017, Virginia Tech
MW 10:10-11:00 Class sessions; Wed 6-8 pm, Project sessions
Classroom: Newman Library Room 124
Instructor: Tom Ewing (etewing@vt.edu)
Office Hours: Monday & Wednesday, 11-12 am
Teaching Assistant: Nick Bolin (nbolin@vt.edu)
Office Hours: Monday, 12-2 pm

Link to Active Syllabus (enrolled Virginia Tech students only): Introduction to Data in Social Context examines the use of data to identify, reveal, explain, and interpret patterns of human behavior, identity, and interactions. An exploration of the historical trajectories of data asks how societies have increasingly identified numerical measures as meaningful categories of knowledge, as well as the persistent challenges to assumptions about the universality of social categories reducible to numerical measures. The course examines the range of information that can be classified as data in the form of quantified measures of social categories, textual collections, sound and visual media, and geographical information. Students will learn how social context shapes the collection, interpretation, and uses of data by examining the changing nature of categories defined by class, ethnicity, race, gender, and other elements of collective and individual identity. The course challenges students to ask how data is being collected, how the data has been used to shape policies, and how the process of analyzing data is shaped by social conditions. An exploration of the limits of data will focus on absences and erasures in social categories, the manipulation of data for particular purposes, and the use of data to replicate and reinforce structures of inequality.

 

Readings:

George Orwell, 1984 (1948, any edition, online text version or google doc version)
David Weinberger, Too Big to Know. Rethinking Knowledge (2012)
John Cheney-Lippold, We Are Data. Algorithms and the Making of Our Digital Selves (2017)
Jordan Ellenberg, How Not to be Wrong. The Power of Mathematical Thinking (2014

 

Assignments:

Problem sets (5 assigned, top 4 grades @ 8%) 32%

Group projects (project 1 = 10%, project 2 = 15%, project 3 = 15%, project 4 = 10%) 50%

Final essay (3% first draft, 6% second draft, 9% final draft) 18%

The final essay is a four page (single spaced) paper explaining why what you learned about data in social context has developed skills, perspective, and capacities needed  for your future. The essay may take the form of 1) a job / graduate school application; 2) newspaper opinion piece; or 3) letter to your parents.

Projects, problem sets, and the essay are designed to advance critical thinking:

  • Understanding the relationship between historical context and categories of data analysis
  • Data collection, analysis, and evaluation as tools for engaged historical thinking
  • Communicating methods, outcomes, and implications of data to external audiences
  • Acquiring, using, and enhancing skills identified as valuable by employers across fields

 

Curriculum for Liberal Education: This course will meet the CLE requirements for Area 5 by teaching students basic skills in quantitative reasoning and problem solving by demonstrating how humans have identified the collection and analysis of data as a tool to address important social questions and problems. This course will meet CLE requirements for Area 2 by exploring how the increasing value attached to data has shaped ideas and identities in the contemporary world.

Presetations on Spanish influenza (link)