PORESFall 2025
CORE CLASSES
Core Competency 1: American Politics
PSCI 0200 Introduction to American Politics
Michele Margolis (TR 1:45 - 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
Matt Levendusky (MW 9 - 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.
ECON 2300 Statistics for Economists
Karun Adusumilli (TR 1:45 - 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 course must be taken in conjunction with STAT 1020 to count to the SRDA minor.
Professor TBA (MW 8:30 - 10 am / 10:15 - 11:45 am / 1:45 - 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 course must be taken in conjunction with STAT 1010 to count to the SRDA minor.
Shuva Gupta (TR 8:30 - 10 am / 10:15 - 11:45 am / 1:45 - 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 course must be taken in conjunction with STAT 1120 to count to the SRDA minor.
Elizabeth Ajazi (TR 1:45 - 2:45 pm / 3:30 - 4:30 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 4300 Probability
Multiple professors (MW 8:30 - 10 am / 10:15 - 11:45 am // TR 10:15 - 11:45 am / 3:30 - 5 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
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
Marc Trussler (MW 10:15 - 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.
ELECTIVES
Below is a list of approved SRDA electives on offer in Fall 2025. This list is not exhaustive; other accepted SRDA electives may be rostered in the 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 stkath@sas.upenn.edu if you’re interested in petitioning an elective.
PSCI 1290 / LALS 1290 Race and Ethnic Politics
Daniel Gillion (TR 10:15 - 11:45 am)
This course examines the role of race and ethnicity in the political discourse through a comparative survey of recent literature on the historical and contemporary political experiences of the four major minority groups (Blacks or African Americans, American Indians, Latinos or Hispanic Americans, and Asian Americans). A few of the key topics will include assimilation and acculturation seen in the Asian American community, understanding the political direction of Black America in a pre and post Civil Rights era, and assessing the emergence of Hispanics as the largest minority group and the political impact of this demographic change. Throughout the semester, the course will introduce students to significant minority legislation, political behavior, social movements, litigation/court rulings, media, and various forms of public opinion that have shaped the history of racial and ethnic minority relations in this country. Readings are drawn from books and articles written by contemporary political scientists.
PSCI 4200 Political Psychology
Michele Margolis (T 3:30 - 6:30 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.
PSCI 4811 Machine Learning in Political Science, Sociology, and Economics
Daniel Gillion (T 1:45 - 4:45 pm)
Technology is quickly changing the way we learn and live, where machine learning and artificial intelligence (A.I.) approaches are becoming dominant tools used to understand big data for social protest events, economic markets, political campaigns, and politicians’ public policy actions. This course introduces students to some of the most popular topics in machine learning. The course teaches students, with no previous knowledge of programming, how to program these techniques and adapt it to their unique research interests. More importantly, it takes a practical approach to applying machine learning to real world situations found in sociology, economics, and political science. In summary, this course explores the application of machine learning (ML) techniques to research questions in political science, sociology, and economics. Students will learn the theory behind machine learning algorithms and gain hands-on experience using R to analyze real-world data.
BEPP 2800 Applied Data Analysis
Yisroel Cahn (MW 10:15 - 11:45 am)
This course will examine how and when data can be used specifically to infer whether there is a causal relationship between two variables. We will emphasize (a) the critical role of an underlying economic theory of behavior in interpreting data and guiding analysis, as well as (b) a range of advanced techniques for inferring causality from data, such as randomized controlled trials, regression discontinuity, difference-in-difference, audit study (mystery shopping) approaches and stock-market event studies. The issue of causality, and the relevance of thinking about models and methods for inferring causality, is just as central and important for "Big Data" as it is when working with traditional data sets in business and public policy. The emphasis will not be on proofs and derivations but rather on understanding the underlying concepts, the practical use, implications and limitations of techniques. Students will work intensively with data, drawing from examples in business and public policy, to develop the skills to use data analysis to make better decisions. All analysis will be conducted using R. The goals of the course are for students to become expert consumers able to interpret and evaluate empirical studies as well as expert producers of convincing empirical analysis themselves.
CIS 5210 Artificial Intelligence
Christopher Callison-Burch (TR 12 - 1:30 pm)
This course investigates algorithms to implement resource-limited knowledge-based agents which sense and act in the world. Topics include, search, machine learning, probabilistic reasoning, natural language processing, knowledge representation and logic. After a brief introduction to the language, programming assignments will be in Python.
CIS 5450 Big Data Analytics
Professor TBA (MW 1:45 - 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
Matthew O’Donnell (TR 1:45 - 3:!5 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.
CRIM 1200 Statistics for the Social Sciences
Maria Cuellar (MW 1:45 - 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).
CRIM 4002 Criminal Justice Data Analytics
Greg Ridgeway (TR 1:45 - 3:15 pm)
This course covers the tools and techniques to acquire, organize, link and visualize complex data in order to answer questions about crime and the criminal justice system. The course is organized around key questions about police shootings, victimization rates, identifying crime hotspots, calculating the cost of crime, and finding out what happens to crime when it rains. On the way to answering these questions, the course will cover topics including data sources, basic programming techniques, SQL, regular expressions, web scraping, and working with geographic data. The course will use R, an open-source, object oriented scripting language with a large set of available add-on packages.
ECON 2310 Econometric Methods and Models
Xu Cheng (MW 10:15 - 11:45 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.
ECON 4330 Econometric Machine Learning Methods and Models
Karun Adusumilli (TR 10:15 - 11:45 am)
This course covers econometric methods, machine learning methods, and their interface, focusing on aspects of estimation, inference, and prediction in causal and non-causal environments. Topics may include Bayesian learning; recursive estimation and optimal filtering; randomized controlled trials and their approximation; latent variables; classification; topic analysis; LDA models; neural networks; random forests; regularization (shrinkage, selection, ...); network estimation and description.
GAFL 6110 Statistics for Public Policy
Samantha Sangenito (MW 10:15 - 11:45 am)
This course is GAFL 6110, the required course in statistical analysis for students in the Fels school. This is the required course in statistical analysis for public policy/public administration. Increasingly, this is a quantitative field. Even if you think you'll someday just be (say) a city manager, and not likely to use quantitative analysis yourself, you will likely find yourself working with quantitative data. For example, "policy evaluation" has become a buzzword in recent years in public management and examples involving Fels graduates-or their equivalents-abound. Did giving low-income children after-school tutoring improve their academic performance? Does expanding a free-lunch program reduce the number of student outbursts in classrooms? Did Philadelphia's "big belly" trash cans actually reduce the amount of litter on our streets? Answering any of these questions requires statistical analysis. This course aims to lay the groundwork for you to answer these (and many more!) questions. The point here is not to convince you to adopt a quantitative design for your own work, or that quantitative designs are the "best" designs for answering all questions. Rather, the goal is to give you a set of tools that will enable you to read, critique and eventually produce your own quantitative research. The course will introduce you to the logic of social scientific inquiry, and the basic statistical tools used to analyze politics and public policy.
MKTG 2120 Data Analysis for Marketing and Communications
Zhenling Jiang (TR 1:45 - 3:15 pm / 3:30 - 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 / STAT 4770 Introduction to Python for Data Science
This is a half-credit course and will be offered during both halves of the fall semester.
Richard Waterman (MW 12 - 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.
SOCI 2010 Social Statistics
Camille Charles (MW 10:15 - 11:45 am)
This course offers a basic introduction to the application/interpretation of statistical analysis in sociology. Upon completion, you should be familiar with a variety of basic statistical techniques that allow examination of interesting social questions. We begin by learning to describe the characteristics of groups, followed by a discussion of how to examine and generalize about relationships between the characteristics of groups. Emphasis is placed on the understanding/interpretation of statistics used to describe and make generalizations about group characteristics. In addition to hand calculations, you will also become familiar with using PCs to run statistical tests.
STAT 4700 Data Analytics and Statistical Computing
Rommel Regis (MW 1:45 - 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
Professor TBA (TR 10:15 am - 11:45 am, 12 pm - 1:30 pm)
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.
STAT 4750 Sample Survey Design
Jeanne Ruane (T 5:15 - 8:15 pm)
This course will cover the design and analysis of sample surveys. Topics include simple sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias. This course may be taken concurrently with the prerequisite with instructor permission.
URBS 2000 Introduction to Urban Research
Ira Goldstein (R 5:15 pm - 8:15 pm)
This course will examine different ways of undertaking urban research. The goal will be to link substantive research questions to appropriate data and research methods. Computer-based quantitative methods, demographic techniques, mapping / GIS and qualitative approaches will be covered in this course. Student assignments will focus on constructing a neighborhood case study of a community experiencing rapid neighborhood change.
URBS 3300 GIS Applications in Social Science
Casey Ross (F 10:15 am - 1:15 pm)
This course will introduce students to the principles behind Geographic Information Science and applications of (GIS) in the social sciences. Examples of GIS applications in social services, public health, criminology, real estate, environmental justice, education, history, and urban studies will be used to illustrate how GIS integrates, displays, and facilitates analysis of spatial data through maps and descriptive statistics. Students will learn to create data sets through primary and secondary data collection, map their own data, and create maps to answer research questions. The course will consist of a combination of lecture and lab.