This course will focus on advanced topics in survey design and analysis. Topics covered include different approaches to sampling, how to construct and use survey weights, and tools for analyzing and enriching survey data, including approaches to conducting matching and multiple imputation, as well as the construction and analysis of panel data. The course will also focus on designing and analyzing survey experiments.

Algorithmic systems are at the center of today's digital world, and mediate communication processes in areas as diverse as social media, journalism, healthcare, and governments. How do algorithmic systems capture, represent, and transmit information about everyday interactions? How do they shape, and are shaped by, social, cultural, and political life? What kind of new issues and concerns arise from their ubiquitous use? This course provides a critical introduction to algorithmic systems, and how they relate to issues of communication, power and inequalities in society.

This course serves as a rigorous introduction to quantitative empirical research methods, designed for doctoral students in social science and master?s students with a data analytics or computational social science focus. The material covered will include a brief introduction to the problem of causality, followed by modules on (1) measurement, (2) prediction, (3) exploratory data analysis (discovery), (4) probability (including distributions of random variables), and (5) uncertainty (including estimation theory, confidence intervals, hypothesis testing, power).

This course introduces students to the basic language of behavioral research, with an emphasis on designing valid social science research. An emphasis is placed on measurement reliability and validity, internal research design validity, and generalizability, or external research design validity.

The theory seminar is a weekly meeting in which topics of interest in the theory of computation - broadly construed - are presented. This is sometimes new research by visitors or local people. It is sometimes work in progress, and it is sometimes recent material of others that some of us present in order to learn and share. This is a one-credit seminar which may be taken repeatedly for credit. May be repeated for credit up to six times.

Teaching Assistants as Tomorrow's Faculty prepares Teaching Assistants (TAs) at the College of Information and Computer Sciences to fulfill their duties in an effective and pedagogically sound manner. The one credit (not repeatable) course is semester long and taken by all TAs prior to assuming assistantship.

Not available at this time

Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science.