Current Course Offerings

FALL 2024




Core Competency 1: American Politics


PSCI 0200 Introduction to American Politics

This class was formerly listed as PSCI 130.

Marc Meredith (TR 1:45 pm - 2:45 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.


Core Competency 2: Statistics


PSCI 1800 Introduction to Data Science

This class was formerly listed as PSCI 107.

Matt Levendusky (MW 9 am - 10 am)

Understanding and interpreting large datasets is increasingly central in political and social science. From polling, to policing, to economic inequality, to international trade, knowing how to work with data will allow you to shed light on a wide variety of substantive topics. This is a first course in a 4-course sequence that teaches students how to work with and analyze data. This class focuses on data acquisition, management, and visualization, the core skills needed to do data science. Leaving this course, students will be able to acquire, input, format, analyze, and visualize various types of political and social science data using the statistical programming language R. 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. Leaving this class, students will be prepared to deepen their R skills in PSCI 3800, and then use their R skills to learn statistics in PSCI 1801 and 3801. They will also be ready to use their R skills in courses in other disciplines as well.


PSCI 1800 Statistical Methods in Political Science

This class was formerly listed as PSCI 338.

Marc Trussler (MW 9 am - 10 am)

This course is designed as a follow-up to PSCI 1800. In that class students learn a great deal about how to work with individual data sets in R: cleaning, tidying, merging, describing and visualizing data. PSCI 1801 shifts focus to the ultimate goal of data science: making inferences about the world based on the small sample of data that we have. Using a methodology that emphasizes intuition and simulation over mathematics, this course will cover the key statistical concepts of probability, sampling, distributions, hypothesis testing, and covariance. The ultimate goal of the class is for students to have the knowledge and ability to perform, customize, and explain bivariate and multivariate regression. Students who have not taken PSCI-1800 should have basic familiarity with R, including working with vectors and matrices, basic summary statistics, visualizations, and for() loops.


ECON 2300 Statistics for Economists

This class was formerly listed as ECON 103.

Karun Adusumilli (TR 1:45 pm - 3:15 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.


STAT 1010 Introductory Business Statistics

This class was formerly listed as STAT 101. This course must be taken in conjunction with STAT 1020 to count to the SRDA minor.

Miyabi Ishihara (MW 8:30 am - 10 am, 10:15 am - 11:45 am, 1:45 pm - 3:15 pm)

Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college. This course may be taken concurrently with the prerequisite with instructor permission.


STAT 1020 Introductory Business Statistics

This class was formerly listed as STAT 102. This course must be taken in conjunction with STAT 1010 to count to the SRDA minor.

Shuva Gupta (TR 8:30 am - 10 am, 12 pm - 1:30 pm, 1:45 pm - 3:15 pm)

Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.


STAT 1110 Introductory Statistics

This class was formerly listed as STAT 111. This course must be taken in conjunction with STAT 1120 to count to the SRDA minor.

Elizabeth Ajazi (TR 12 pm - 1 pm, 1:45 pm - 2:45 pm)

Introduction to concepts in probability. Basic statistical inference procedures of estimation, confidence intervals and hypothesis testing directed towards applications in science and medicine. The use of the JMP statistical package. Knowledge of high school algebra is required for this course.


STAT 1120 Introductory Statistics

This class was formerly listed as STAT 112. This course must be taken in conjunction with STAT 1110 to count to the SRDA minor.

Paul Sabin (MW 12 pm - 1:30 pm)

Further development of the material in STAT 1110, in particular the analysis of variance, multiple regression, non-parametric procedures and the analysis of categorical data. Data analysis via statistical packages. This course may be taken concurrently with the prerequisite with instructor permission.


STAT 4300 Probability

This class was formerly listed as STAT 430.

Multiple professors (MW 8:30 am - 10 am, 10:15 am - 11:45 am, 12 pm - 1:30 pm; TR 3:30 pm - 5 pm, 5:15 pm - 6:45 pm)

Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.


Core Competency 3: Survey Research


PSCI 3800 Applied Data Science

This class was formerly listed as PSCI 207.

Stephen Pettigrew (MW 1:45 pm - 3:15 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. 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.


PSCI 3802 Political Polling

This class was formerly listed as PSCI 333.

Will Marble (MW 10:5 am - 11:45 am)

Political polls are a central feature of elections and are ubiquitously employed to understand and explain voter intentions and public opinion. This course will examine political polling by focusing on four main areas of consideration. First, what is the role of political polls in a functioning democracy? This area will explore the theoretical justifications for polling as a representation of public opinion. Second, the course will explore the business and use of political polling, including media coverage of polls, use by politicians for political strategy and messaging, and the impact polls have on elections specifically and politics more broadly. The third area will focus on the nuts and bolts of election and political polls, specifically with regard to exploring traditional questions and scales used for political measurement; the construction and considerations of likely voter models; measurement of the horserace; and samples and modes used for election polls. The course will additionally cover a fourth area of special topics, which will include exit polling, prediction markets, polling aggregation, and other topics. It is not necessary for students to have any specialized mathematical or statistical background for this course.



Below is a list of approved SRDA electives on offer in Fall 2024. This list is not exhaustive; other accepted SRDA electives may be rostered this fall. Students are also able to petition electives to the SRDA faculty committee if they feel a course should count toward their minor. Please reach out to SRDA advisor Katie Steele at if you’re interested in petitioning an elective.


CIS 5450 Big Data Analytics

This class was formerly listed as CIS 545.

Ryan Marcus (MW 1:45 pm - 3:15 pm)

In the new era of big data, we are increasingly faced with the challenges of processing vast volumes of data. Given the limits of individual machines (compute power, memory, bandwidth), increasingly the solution is to process the data in parallel on many machines. This course focuses on the fundamentals of scaling computation to handle common data analytics tasks. You will learn about basic tasks in collecting, wrangling, and structuring data; programming models for performing certain kinds of computation in a scalable way across many compute nodes; common approaches to converting algorithms to such programming models; standard toolkits for data analysis consisting of a wide variety of primitives; and popular distributed frameworks for analytics tasks such as filtering, graph analysis, clustering, and classification. Recommended: broad familiarity with probability and statistics, as well as programming in Python. Additional background in statistics, data analysis (e.g., in Matlab or R), and machine learning is helpful (example: ESE 5420).


COMM 2100 Quantitative Research Methods in Communication

This class was formerly listed as COMM 210.

John Jemmott (MW 12 pm - 1:30 pm)

This course is a general overview of the important components of social research. The goal of the course is to understand the logic behind social science research, be able to view research with a critical eye and to engage in the production of research. It will cover defining research problems, research design, assessing research quality, sampling, measurement, and causal inference. The statistical methods covered will include descriptive and inferential statistics, measures of association for categorical and continuous variables, inferences about means, and the basic language of data analysis. Course activities will include lectures, class exercises, reading published scientific articles, using statistical software, and discussing research featured in the news.


COMM 3130 Computational Text Analysis for Communication Research

This class was formerly listed as COMM 313.

Matthew O’Donnell (TR 1:45 pm - 3:15 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.


CRIM 1200 Statistics for the Social Sciences

This class was formerly listed as CRIM 250.

Maria Cuellar (MW 1:45 pm - 3:15 pm)

Statistical techniques and quantitative reasoning are essential tools for properly examing questions in the social sciences. This course introduces students to the concepts of probability, estimation, confidence intervals, and how to use the statistical concepts and methods to answer social science questions. The course will require the use of R, a free, open source statistical analysis program. This course has been approved for the quantitative data analysis requirement (QDA).


ECON 2310 Econometric Methods and Models

This class was formerly listed as ECON 104.

Xu Cheng (MW 8:30 am - 10 am)

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. 

Zhenling Jiang (TR 10:15 am - 11:45 am, 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.


OIDD 4770 Introduction to Python for Data Science

This class was formerly listed as OIDD 477. This is a half-credit course and would need to be taken in conjunction with another half-credit course to fulfill an elective requirement.

Richard Waterman (MW 12 pm - 1:30 pm)

The goal of this course is to introduce the Python programming language within the context of the closely related areas of statistics and data science. Students will develop a solid grasp of Python programming basics, as they are exposed to the entire data science workflow, starting from interacting with SQL databases to query and retrieve data, through data wrangling, reshaping, summarizing, analyzing and ultimately reporting their results. Competency in Python is a critical skill for students interested in data science. Prerequisites: No prior programming experience is expected, but statistics, through the level of multiple regression is required. This requirement may be fulfilled with Undergraduate courses such as Stat 1020, Stat 1120.


PSCI 1202 Changing American Electorate

This class was formerly listed as PSCI 234.

Dan Hopkins (MW 10:15 am - 11:15 am)

In 1960, a Democratic candidate won a very narrow Presidential victory with just 100,000 votes; in 2000, the Democratic candidate lost but received 500,000 more votes than his opponent. Still, contemporary scholars and journalists have made a variety of arguments about just how much the American political landscape changed in the intervening 40 years, often calling recent decades a transformation. This course explores and critically evaluates those arguments. Key questions include: how, if at all, have Americans political attitudes and ideologies changed? How have their connections to politics changed? What has this meant for the fortunes and strategies of the two parties? How have the parties' base voters and swing voters changed? What changes in American society have advantaged some political messages and parties at the expense of others? Focusing primarily on mass-level politics, we consider a wide range of potential causes, including the role of race in American politics, suburbanization, economic transformations, the evolving constellation and structure of interest groups, declining social capital, the changing role of religion, immigration, and the actions of parties and political elites. For three weeks in the semester, we will take a break from considering broader trends to look at specific elections in some depth.


PSCI 4200 Political Psychology

This class was formerly listed as PSCI 436.

Michele Margolis (1:45 pm - 4:45 pm)

How do campaign advertisements influence voters' perceptions and behavior? What roles do emotions play in politics? Do we all harbor some measure of racism, sexism, or homophobia, and what role do these stereotypes play in political behavior? How and why do ideologies form, and how does partisanship influence the way that voters understand the political world? How do people perceive threat, and what are the psychological consequences of terrorism? These questions, and many others, are the province of political psychology, an interdisciplinary field that uses experimental methods and theoretical ideas from psychology as tools to examine the world of politics. In this course, we will explore the role of human thought, emotion, and behavior in politics and examine the psychological origins of citizens' political beliefs and actions from a variety of perspectives. Most of the readings emphasize politics in the United States, though the field itself speaks to every aspect of political science.


SOCI 2000 Sociological Research Methods

This class was formerly listed as SOCI 100.

Staff Instructor (MW 12 - 1 p.m.)

One of the defining characteristics of all the social sciences, including sociology, is a commitment to empirical research as the basis for knowledge. This course is designed to provide you with a basic understanding of research in the social sciences and to enable you to think like a social scientist. Through this course students will learn both the logic of sociological inquiry and the nuts and bolts of doing empirical research. We will focus on such issues as the relationship between theory and research, the logic of research design, issues of conceptualization and measurement, basic methods of data collection, and what social scientists do with data once they have collected them. By the end of the course, students will have completed sociological research projects utilizing different empirical methods, be able to evaluate the strengths and weaknesses of various research strategies, and read (with understanding) published accounts of social science research.


STAT 4700 Data Analytics and Statistical Computing

This class was formerly listed as STAT 470.

Rommel Regis (MW 1:45 pm - 3:15 pm)

This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Waiving the Statistics Core completely if prerequisites are not met. This course may be taken concurrently with the prerequisite with instructor permission.


STAT 4710 Modern Data Mining

This class was formerly listed as STAT 471.

Krishna Padmanabhan (TR 3:30 pm - 5pm)

With the advent of the internet age, data are being collected at unprecedented scale in almost all realms of life, including business, science, politics, and healthcare. Data mining—the automated extraction of actionable insights from data—has revolutionized each of these realms in the 21st century. The objective of the course is to teach students the core data mining skills of exploratory data analysis, selecting an appropriate statistical methodology, applying the methodology to the data, and interpreting the results. The course will cover a variety of data mining methods including linear and logistic regression, penalized regression (including lasso and ridge regression), tree-based methods (including random forests and boosting), and deep learning. Students will learn the conceptual basis of these methods as well as how to apply them to real data using the programming language R. This course may be taken concurrently with the prerequisite with instructor permission.