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Research Methods - PS 170 through 181

170. Algorithms, Public Policy, and Ethics (4)

The deployment of predictive algorithms for public policy optimization has increased in recent years across a variety of domains. This course will introduce key concepts, considerations, and concerns relating to algorithmic decision-making in policy processes. This includes understanding how data are used to make predictions, how predictions can be used to aid decision-making, the conditions under which these practices will be effective, and the important ethical dimensions of these practices.

170A. Applied Data Analysis for Political Science (4)

This course is an advanced introductory course for undergraduates. It will acquaint students with statistical methodology as it is used in the social sciences.  It is assumed that the student has the mathematical background to progress through the materials a bit faster than in a true introductory course.

171. Making Policy with Data (4)

This class explores how we can make policy recommendations using data. We attempt to establish causal relationships between policy interventions and outcomes based on statistical evidence. Hands-on examples will be provided throughout the course. Prerequisite: POLI 5(D)/ECON 5 and POLI 30 or 30D

172. Advanced Social Data Analytics (4)

An accelerated course in computer programming and data analytics for collecting, analyzing, and understanding data in the social world. Students engage in hands-on learning with applied social science problems, developing statistical and computational skills for sophisticated data manipulation, analysis, visualization. Prerequisite: POLI 5(D)/ECON 5  and POLI 30 or 30D, or consent of instructor.

173. Social Network Analysis (4)

This class introduces tools for analyzing social networks including graph visualization, egocentric and sociocentric network measures, network simulation, and data management.

174. Analyzing Elections (4)

This course is concerned with the political economy of elections. It will analyze key topics in political science using both theoretical and empirical tools. Specific topics covered include: party and candidate strategy, electoral campaigns, the role and origins of parties, electoral accountability, and voter behavior.

175. Machine Learning for Social Scientists (4)

This course focuses on statistical and algorithmic techniques to analyze and utilize large collections of data for social science inferences. The goal of the course is to introduce students to modern machine learning methods and provide the skills necessary to apply the methods widely. In achieving this goal, students will learn about core concepts in machine learning and statistics, developing skills that are transferable to other types of data and inference problems.

176. Text as Data (4)

This class explores statistical and computational methods to enable students to use text as a data source in the social sciences. Hands-on examples will equip students to work with text data in final projects.

179. Special Topics in Political Science Methodology (4)

Special topics course in quantitative methods for political science and broader social sciences.

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