Data Driven Societies

Eric Gaze and Jen Jack Gieseking

What’s it about? In this course, we explore the possibilities, limitations, and implications of using digital and computational methods and analytics to study issues that affect our everyday lives from a social scientific approach. We pay special attention to the ways we collect, trust, analyze, portray, and use data, most especially the tools and meanings involved in data visualization and modeling.

What? This course tackles a number of cutting-edge issues and questions that confront society today: What sorts of questions can be asked and answered using digital and computational methods to rethink our relationships to data and what can data can show us about the world? How do we construct models to help us better understand social phenomena and associated data? What is data, and how do we know it’s reliable? How do these methods complement and sometimes challenge traditional methodologies in the social sciences? Students will leave the course with substantive experience in both digital and computational methods. Students will learn how to apply a critical lens for understanding and evaluating what computers can (and cannot) bring to the study of society.

Why? Big data and computational methods, such as changes in social media privacy laws and advances in mapping and network analysis, are changing financial markets, political campaigning, and higher education and becoming commonplace in our lives. Our daily existence is increasingly structured by code, from the algorithms that time our traffic lights to those that filter our search criteria and record our thoughts and ideas. Why do we develop and use data analytic tools in the ways that we do? What historical and social constructs are in place that impact the creation of such tools and methods, and why are these approaches given priority over alternatives?

How? This class is a project-based, collaborative learning experience and experiment. For most of the semester there will be regular class readings for which students are responsible and a number of short assignments, including blog posts and comments, in-class quizzes, lab projects, and a hackathon. This will prepare students for the capstones of the course: an independent research paper and presentation, and an exam. Throughout the semester, time will be devoted to developing and implementing these projects in a series of scaffolded labs, posts, and quizzes. Students will engage in the process of data analysis (statistical, spatial, and visual) to set criteria for, build, and then evaluate their own project’s impact, all drawing from a social science research approach. The course assignments will be structured around a social justice Twitter hashtag of their choice within the topics of privacy, environment, identity, economics, or politics. In addition to scraping data from Twitter, data will be researched and incorporated into their projects. Students will conduct statistical analyses and craft visual displays of their data using Excel and R; create maps using TileMill, Mapbox, BatchGeo, and CartoDB; and conduct network analyses using Gephi. Students will gain a basic understanding of how to use all of these programs and a comfort in working in a digital and computational environment (including basic scripting in both R and Python). Students will develop a more robust understanding of what data visualization and data analyses affords one in engaging with and acting on an issue.