Data Studies: Data in Society

Data Studies: Data in Society courses prepare students to take their critical thinking and analysis skills and apply them to data-oriented environments, including industry, government, and non-profit organizations.
 

Science and Technology Studies and the DataLab have developed a new program in Data Studies: Data in Society with courses open to students from all majors. These courses are designed to offer critical understanding of the social life of data, as well as important aspects of data analysis. They prepare students to apply their critical thinking and analytical skills to data-oriented environments in industry, government, and non-profit organizations.

Learn more about the program by visiting these websites:

College of Letters and Science page on Data Studies: Data in Society

Data Studies Courses

  • STS 101: Data and Society
  • Introduction to Data Science, Data Studies, and Data Justice in Society

    STS 101 course incorporates methods from STS and concepts from the social sciences and humanities, focusing on critical approaches to data. The course involves four hours of lecture/discussion per week as well as homework involving critical reading, data exploration and analysis, and presentation of work. Topics include:

    Caring for Data (structuring and naming data files informatively, backing up original data, ability to reproduce cleaned data, sharing data, documenting data content and provenance)

    The Data Science Process in the World (defining a problem, clarifying a problem, learning to how and where to ask questions, learning to be comfortable not knowing, learning to experiment and explore data, teamwork on assignments)

    Data Exploration and Manipulation (indexes, lookup tables, pivot tables, graphing and charting)

    Understanding Data (archaeology of data, metadata concepts, formats, columns, features, data integrity, cleaning data)

    Stakeholders (analysis, constraints and benefits, interviewing, roleplaying, redefining questions)

    Big Data and Pre-Machine Learning (brainstorming features, selecting good data, big data strengths and limitations)

    Presenting/Visualizing Rhetoric (graph and chart approaches, structuring a presentation, structuring a report)

  • STS 112: Visualizing Society with Data
  • Visualizing Society

    Analysis and visualization of historical and contemporary data about populations and societies using R. Critical exploration of visual communication of information about people over time and critical assessment of role of data collection and analysis in societies. DD, QL, SS.

  • STS 115: Data Sense and Exploration: Critical Storytelling and Analysis
  • Data Exploration and Data Storytelling — How to Make Strong Data Narratives
     
    This course introduces students to data science analysis through case studies of working with data to develop and tell meaningful stories about interesting questions. Students work with real questions and real-world (messy) data, leaning to think critically about how to quantify and measure concepts. The course shows how to visualize data for exploratory data analysis (EDA) and for communicating final results to different types of audiences. The students develop data literacy, intuition about sampling variability, skepticism about quantitative claims, best practices in data visualization, and an introduction to programming.

    We use case studies to explore problems with real data that require students to think about the context of the problem and the data. The case studies also encourage the students to bring in other sources of data that may offer improved insights into the problem. They also raise issues with “found” data that are not randomly sampled and how naive inferences can be highly misleading. Students will learn to clean data, considering how the data were collected and how errors may have been introduced.

    The course also introduces the R computing environment for data analysis. Topics will also include: network architecture; file system and command line basics; version control; data structures and types; webscraping; data types; and using the R computing environment for data exploration, cleaning, analysis, and visualization. We focus on the historical and social contexts of data analysis, emphasizing narrative and the development of data storytelling skills.
     
  • STS 195: Research in Data Studies
  • Capstone Research Seminar and Research Project in Data Studies

    This seminar course focuses on analysis of real-world data in the form of ongoing active research projects. The course emphasizes teamwork in the identification of problems, methods, and implementation. Students are embedded into research teams with classmates, data scientists, and external research collaborators from across the university. Projects entail data gathering, cleaning, exploration, analysis, and visualization using R; and interpretation and presentation of results in oral, visual, and textual formats to a variety of researchers and community members.