Visualization

This graduate course will train you in the methods, conceptual approaches, and theories of computational social science. It is set up to welcome people from many different backgrounds, particularly those with or without prior exposure to programming, statistics, or the social sciences. You will learn how to design research projects and answer research questions motivated and situated in social sciences discourses and theories. The course will survey canonical and cutting-edge methods and techniques. This includes methodological approaches in Big Data analysis, data visualization, social network analysis, agent-based modeling, and natural language processing. You will learn to identify and develop variables, mechanisms, and theoretical framing grounded and motivated within the social sciences. We will also address the growing ethical challenges and considerations associated with computational social science methods and approaches.

When people use the internet, they leave behind traces of their political behaviors and social interactions across space and time. While these digital traces are typically created and collected by businesses and governments for their own internal purposes, they are often available to researchers either incidentally or explicitly on behalf of these organizations. Digital trace data have the potential to broaden the scope and scale of political and social research, but require knowledge of computational tools and methods that are typically not taught to social scientists. Moreover, digital trace data present new legal and ethical considerations, since they often contain sensitive, individual-level information. Digital trace data also present conceptual challenges, some which are not new to political and social scientists, like representativeness, and others that are unique to the web, including algorithmic confounding.

This course introduces theories and methods used for the analysis of political networks. Political networks describe how political actors - such as participants of the policy process - form and maintain relationships, and the analysis of political networks can help us to understand how these relationships influence political or policy outcomes. Network concepts are increasingly prevalent across a wide range of social science disciplines, and are often used as a tool to study complex phenomenon such as cooperation, diffusion of innovation, and social capital. This course will introduce students to major research questions in the study of networks, as well as their applicability to understand real-world problems in public policy and political science. Students will learn, through hands-on training in R, how to manage network data and perform essential descriptive and inferential analyses on these data.

Course is a graduate-level seminar in Advanced Research Communication Methods. Students will read primary research in Communication relating to Research Methods and learn the key theoretical perspectives in the area. They will become familiar with current areas of interest in the topic area and future directions. Course will involve lecture, discussion, and the production of graduate level coursework. Specific content areas will vary by semester and instructor.

Contributions of the ethnography of communication, language variation studies, and conversation/discourse analysis to the interdisciplinary development of sociolinguistics.

Qualitative and quantitative methods for research on social networks (the patterning of relations among actors ranging from persons to nations), emphasizing a "how-to" approach to analyzing existing datasets or those collected by seminar participants in their own research.

Formal models for the study of cultural fields, social and cultural logics, cultural classification, the mutual shaping of networks and culture, modeling of cultural toolkits, narrative modeling, and analysis of ethnographic data.

This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and data mining trends and research frontiers. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course include laboratory exercises, with data mining case studies using data from many different resources such as social networks, linguistics, geo-spatial applications, marketing and/or psychology.

Machine learning describes the development of algorithms which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on example data. These examples can be provided by a human, or they can be gathered automatically as part of the learning algorithm itself. This course will introduce the fundamentals of machine learning, will describe how to implement several practical methods for pattern recognition, feature selection, clustering, and decision making for reward maximization, and will provide a foundation for the development of new machine learning algorithms.

The field of Human-Computer Interaction (HCI) encompasses the design, implementation, and evaluation of interactive computing systems. This course will provide a survey of HCI theory and practice. The course will address the presentation of information and the design of interaction from a human-centered perspective, looking at relevant perceptive, cognitive, and social factors influencing in the design process. It will motivate practical design guidelines for information presentation through Gestalt theory and studies of consistency, memory, and interpretation. Technological concerns will be examined that include interaction styles, devices, constraints, affordances, and metaphors. Theories, principles and design guidelines will be surveyed for both classical and emerging interaction paradigms, with case studies from practical application scenarios. As a central theme, the course will promote the processes of usability engineering, introducing the concepts of participatory design, requirements analysis, rapid prototyping, iterative development, and user evaluation. Both quantitative and qualitative evaluation strategies will be discussed. This course is co-convened: Upper-level undergraduates and graduate students are encouraged to enroll. Graduate students will be expected to complete more substantial projects and will be given more in-depth reading assignments.