Core Competency 1: American Politics
PSCI 130 Introduction to American Politics
Santiago Lujan Cunial (R 5:00 pm - 8:00 pm)
This course is intended to introduce students to the national institutions and political processes of American government. What are the historical and philosophical foundations of the American Republic? How does American public policy get made, who makes it, and who benefits? Is a constitutional fabric woven in 1787 good enough for today? How, if at all, should American government be changed, and why? What is politics and why bother to study it? If these sorts of questions interest you, then this course will be a congenial home. It is designed to explore such questions while teaching students the basics of American politics and government.
PSCI 236 Public Policy Process
Marc Meredith (MW 10:00 am - 11:00 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 107 Introduction to Data Science
Marc Trussler (MW 1:00 pm - 2:00 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.
Core Competency 3: Survey Research and Design
PSCI 207 Applied Data Science
John Lapinski, Stephen Pettigrew, Samantha Sangenito (TR 10:30 am -12:00 pm)
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. Students will learn the skills required to conduct research using surveys and experiments, and will further develop their programming abilities in R. 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. It is expected that students come in having some experience using R, which can be acquired by taking either PSCI 107, PSCI 338 or an equivalent course.
PSCI 332 Survey Research and Design
David Dutwin (R 3:00 pm - 6:00 pm)
Survey research is a small but rich acadmic field and discipline, drawing on theory and practice from many diverse fields including political science and communication. This course canvasses the science and practice of survey methods,sampling theory, instrument development and operationalization, and the analysis and reporting of survey data. Major areas of focus include measurement and research on survey errors, application to election polling, new frontiers in data collection, overall development of data management and introductory statistics. Equivalent R based course if prerequisite not met.
PSCI 498 How Divided Is America
Matthew Levendusky (T 1:30 pm - 4:30 pm)
This class explores whether or not America, and its politics, are divided. Is the American public polarized? What about political elites? Is there any connection between mass and elite polarization? What do we even mean when we say some group is “polarized”? This class will explore these questions in some detail. We will begin at the elite level and ask whether or the political class is now more polarized than it was a half century ago. The answer will be a fairly unambiguous “yes.” We’ll then explore several different explanations for why elites have become more divided since mid-century. After that, we’ll turn our attention to the mass public. The situation there will be considerably more complicated, with evidence both for and against polarization. We’ll explore this evidence in some detail and try to document the ways in which the American public has—and has not—become more polarization over time, paying attention to differences based on issues as well as affect/sentiment toward the other party. Finally, we’ll conclude by exploring the effects of polarization on the legislative process and the mass electorate, and ask what (if anything) can or should be done about polarization.
PSCI 498 Election Law and the 2020 Election
Marc Meredith (W 2:00 pm - 5:00 pm)
The 2020 elections highlighted the importance of the laws governing the administration of elections in the United States. This class consists of two parts. First, we will learn about how the constitution, important historical laws, and court cases have structured the conduct of elections in the United States. Second, we will examine how these laws guided how election administrators and courts thought about changes to election administration during the 2020 primaries and the presidential election because of COVID-19. Grades will be based on class participation, an in-class presentation, and two writing assignments.
GAFL 531 Data Science For Public Policy
Samantha Sangenito (MW 10:30 am - 11:50 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 are learning in GAFL 640: Program Evaluation and Data Analysis. This is a graduate level course and while GAFL 640 is not a pre-requisite, students are expected to have a foundation of data management and analysis before beginning this course. Students should have taken a course with R. Fels and other graduate students receive registration preference, though undergraduate students may request registration via email: firstname.lastname@example.org.
COMM 113 Data Science For Beginners
Yphtach Lelkes (R 3:00 pm - 4:30 pm)
This course serves as an entrance to the world of data science and is aimed at students who have little to no background in data science, statistics, or programming. The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modeling, and effective communication of results. This course, which will rely on R, the statistical programming language, will prepare students for more advanced data science and computational social science courses.
COMM 125 Communication Behavior
Michael Delli Carpini (MW 11:00 am - 12:00 pm)
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 (MW) as a lecture and once a week (F) 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. In addition to fulfilling General Education Curriculum Sector 1 Requirement (Society), this course fulfills one of the two introductory-level courses required of Communication majors or prospective majors.
COMM 313 Computational Text Analysis
Matthew Brook O'Donnell (TR 12:00 pm - 1:30 pm)
In this 'big data' era, presidents and popes tweet daily. Anyone can broadcast their thoughts and experiences through social media. Speeches, debates and events are recorded in online text archives. The resulting explosion of available textual data means that journalists and marketers summarize ideas and events by visualizing the results of textual analysis (the ubiquitous 'word cloud' just scratches the surface of what is possible). Automated text analysis reveals similarities and differences between groups of people and ideological positions. In this hands-on course students will learn how to manage large textual datasets (e.g. Twitter, YouTube, news stories) to investigate research questions. They will work through a series of steps to collect, organize, analyze and present textual data by using automated tools toward a final project of relevant interest. The course will cover linguistic theory and techniques that can be applied to textual data (particularly from the fields of corpus linguistics and natural language processing). No prior programming experience is required. Through this course students will gain skills writing Python programs to handle large amounts of textual data and become familiar with one of the key techniques used by data scientists, which is currently one of the most in-demand jobs.