Current Course Offerings





Core Competency 1: American Politics

PSCI 1200/PPE 3002 Public Policy Process

This class was formerly listed as PSCI 130.

Marc Meredith (MW 10:15 am - 11:15 am)

This course introduces students to the theories and practice of the policy-making process. There are four primary learning objectives. First, understanding how the structure of political institutions matter for the policies that they produce. Second, recognizing the constraints that policy makers face when making decisions on behalf of the public. Third, identifying the strategies that can be used to overcome these constraints. Fourth, knowing the toolbox that is available to participants in the policy-making process to help get their preferred strategies implemented. While our focus will primarily be on American political institutions, many of the ideas and topics discussed in the class apply broadly to other democratic systems of government.


Core Competency 2: Statistics

PSCI 1800 Introduction to Data Science

This class was formerly listed as PSCI 107.

Marc Trussler (MW 12 pm - 1 pm)

Understanding and interpreting large, quantitative data sets is increasingly central in political and social science. Whether one seeks to understand political communication, international trade, inter-group conflict, or other issues, the availability of large quantities of digital data has revolutionized the study of politics. Nonetheless, most data-related courses focus on statistical estimation, rather than on the related but distinctive problems of data acquisition, management and visualization--in a term, data science. This course addresses that imbalance by focusing squarely on data science. Leaving this course, students will be able to acquire, format, analyze, and visualize various types of political data using the statistical programming language R. This course is not a statistics class, but it will increase the capacity of students to thrive in future statistics classes. While no background in statistics or political science is required, students are expected to be generally familiar with contemporary computing environments (e.g. know how to use a computer) and have a willingness to learn a variety of data science tools. You are encouraged (but certainly not required) to register for both this course and PSCI 1801 at the same time, as the courses cover distinct, but complimentary material.


PSCI 1801 Statistical Methods PSCI

This class was formerly listed as PSCI 338.

Samantha Sangenito and Marc Trussler (MW 1:45 pm - 3:15 pm)

The goal of this class is to expose students to the process by which quantitative political science research is conducted. The class will take us down three separate, but related tracks. Track one will teach some basic tools necessary to conduct quantitative political science research. Topics covered will include descriptive statistics, sampling, probability and statistical theory, and regression analysis. However, conducting empirical research requires that we actually be able to apply these tools. Thus, track two will teach us how to implement some of these basic tools using the computer program R. However, if we want to implement these tools, we also need to be able to develop hypotheses that we want to test. Thus, track three will teach some basics in research design. Topics will include independent and dependent variables, generating testable hypotheses, and issues in causality. You are encouraged to register for both this course and PSCI 1800 at the same time, as the courses cover distinct but complementary material. But there are no prerequisites nor is registering for PSCI 1800 necessary, in order to take this course. The class satisfies the College of Arts and Sciences Quantitative Data Analysis (QDA) requirement.


Core Competency 3: Survey Research

PSCI 3800 Applied Data Science

This class was formerly listed as PSCI 207.

William Marble (MW 3:30 pm - 5 pm)

Samantha Sangenito and Stephen Pettigrew (TR 10:15 am - 11:45 am)

Jobs in data science are quickly proliferating throughout nearly every industry in the American economy. The purpose of this class is to build the statistics, programming, and qualitative skills that are required to excel in data science. The substantive focus of the class will largely be on topics related to politics and elections, although the technical skills can be applied to any subject matter.




CIS 1050 Computational Data Exploration

This class was formerly listed as CIS 105.

Arvind Bhusnurmath (MWF 10:15 am - 11:15 am)

The primary goal of this course is to introduce computational methods of interacting with data. In this course, students will be introduced to the IPython programming environment. They will learn how to gather data, store it in appropriate data structures and then either write their own functions or use libraries to analyze and then display the salient information in that data. Data will be drawn from a variety of domains, including but not limited to travel, entertainment, politics, economics, biology etc.


COMM 1250 Introduction to Communication Behavior

This class was formerly listed as COMM 125.

Yphtach Lelkes (MW 10:15 am - 11:15 am)

This course introduces students to social science research regarding the influence of mediated communication on individual and collective attitudes, beliefs, and behaviors. Throughout the semester we explore the impacts of various types of mediated content (e.g., violence, gender and sexuality, race and ethnicity, politics and activism, health and wellbeing); genres (e.g., news, entertainment, educational, marketing); and mediums (e.g., television, film, social media) on what we think and how we act. The aim of the course is to provide students with (1) a general understanding of both the positive and negative effects of mediated communication on people’s personal, professional, social, and civic lives; and (2) the basic conceptual tools needed to evaluate the assumptions, theories, methods, and empirical evidence supporting these presumed effects. Class meets twice a week as a lecture and once a week in smaller discussion groups led by graduate teaching fellows. In addition to a midterm exam and occasional short assignments, students have the option of producing a multi-media capstone project or a final term paper on a media-effects topic of their choice. Group projects or final papers are permitted, with approval of the instructor.


GAFL 5310 Data Science for Public Policy

This class was formerly listed as GAFL 531.

Samantha Sangenito (MW 10:15 am - 11:45 am)

In the 21st century, Big Data surround us. Data are being collected about all aspects of our daily lives. To improve transparency and accountability an increasing number of public organizations are sharing their data with the public. But data are not information. You need good information to make sound decisions. To be an effective public leader, you will need to learn how to harness information from available data. This course will introduce you to key elements of data science, including data transformation, analysis, visualization, and presentation. An emphasis is placed on manipulating data to create informative and compelling analyses that provide valuable evidence in public policy debates. We will teach you how to present information using interactive apps that feature software packages. As in all courses at Fels, we will concentrate on more practical skills than theoretical concepts behind the techniques. This course is designed to expand upon core concepts in data management and analysis that you learned in GAFL 6400: Program Evaluation and Data Analysis. This is a graduate level course and while GAFL 6400 is not a pre-requisite, students are expected to have a foundation of data management and analysis before beginning this course. This class is for graduate and Professional students. Permit requests from undergraduate students will be considered on a case-by-case basis.


ECON 0420 Political Economy

This class was formerly listed as ECON 032.

Deniz Selman (TR 12 pm - 1:30 pm)

This course examines the effects of strategic behavior on political outcomes and government policies. Topics and applications may include voting behavior, candidate competition, voting systems, social choice and welfare, policy divergence, redistributive policies and theories of political transitions.


ECON 4420 Political Economy

This class was formerly listed as ECON 232.

Juliette Fournier (MW 10:15 am - 11:45 am)

This course examines the political and economic determinants of government policies. The course presents economic arguments for government action in the private economy. How government decides policies via simple majority voting, representative legislatures, and executive veto and agenda-setting politics will be studied. Applications include government spending and redistributive policies.


ECON 2300 Statistics for Economists

This class was formerly listed as ECON 103.

Karun Adusumilli (TR 12 pm - 1:30 pm)

The course focuses on elementary probability and inferential statistical techniques. The course begins with a survey of basic descriptive statistics and data sources and then covers elementary probability theory, sampling, estimation, hypothesis testing, correlation, and regression. The course focuses on practical issues involved in the substantive interpretation of economic data using the techniques of statistical inference. For this reason empirical case studies that apply the techniques to real-life data are stressed and discussed throughout the course, and students are required to perform several statistical analyses of their own.


ECON 2310 Econometric Methods and Models

This class was formerly listed as ECON 104.

Xu Cheng (MW 1:45 pm - 3:15 pm)

This course focuses on econometric techniques and their application in economic analysis and decision-making, building on ECON 2300 to incorporate the many regression complications that routinely occur in econometric environments. Micro-econometric complications include nonlinearity, non-normality, heteroskedasticity, limited dependent variables of various sorts, endogeneity and instrumental variables, and panel data. Macro-econometric topics include trend, seasonality, serial correlation, lagged dependent variables, structural change, dynamic heteroskedasticity, and optimal prediction. Students are required to perform several econometric analyses in a modern environment such as R.


MKTG 2120 Data and Analysis for Marketing Decisions

This class was formerly listed as MKTG 212.

Ryan Dew (MW 3:30 pm - 5 pm)

This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.