PORESSpring 2026
Core Competency 1: American Politics
PSCI 1200 / PPE 3002 Public Policy Process
Marc Meredith (MW 10:15 - 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.
PSCI 1201 Public Opinion and American Democracy
Michele Margolis (TR 1:45 - 3:15 pm)
This course examines public opinion in the American political system. We will discuss how to measure public opinion, how citizens formulate opinions, and the role of public opinion in campaigns, elections, and policymaking. We will also consider normative questions, including the role opinion should play in American democracy. Additionally, over the course of the semester we will track public opinion polls related to ongoing elections as well as develop analytical skills to answer questions using public opinion.
Core Competency 2: Statistics
PSCI 1800 Introduction to Data Science
Marc Trussler (MW 12 - 1 pm)
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
Wayne Gao (TR 12 - 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.
STAT 1010 Introductory Business Statistics
This course must be taken in conjunction with STAT 1020 to count to the SRDA minor.
Susanna Lange (TR 10:15 - 11:45 am / 12 - 1:30 pm / 3:30 - 5 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 (MW 10:15 - 11:45 am / 1:45 - 3:15 pm / 3:30 - 5 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 1120 Introductory Statistics
This course must be taken in conjunction with STAT 1110 to count to the SRDA minor.
Rommel Regis (MW 12 - 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
Multiple professors (MW 12 - 1:30 pm / 3:30 - 5 pm // TR 12 - 1:30 pm / 1:45 - 3:15 pm / 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.
ELECTIVES
Below is a list of approved SRDA electives on offer in Spring 2026. This list is not exhaustive; other accepted SRDA electives may be rostered in the spring. 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 4993 Democracy at Work: Election Administration in the US
Al Schmidt (R 5:15 - 8:15 pm)
In the current hyper-partisan climate, there is great interest in the state of American democracy. Elections form the most basic component of government because it is the process by which we select the individuals who govern our public institutions, from school boards and county commissioners to governors and presidents. In this course, we will learn about how elections are administered across the United States and delve into the origins of our electoral processes. We will discuss critical issues surrounding elections, including voter accessibility, election integrity, and the environment in which election administrators currently operate.
CIS 1050 Computational Data Exploration
Arvind Bhusnurmath (MWF 10:15 - 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.
CIS 5450 Big Data Analytics
Ryan Marcus and Harry Smith (TR 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 (MW 3:30 - 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.
COMM 3180 Stories from Data: Programming for Data Journalism
Matthew O’Donnell (MW 10:15 - 11:45 am)
Today masses of data are available everywhere, capturing information on just about everything and anything. Related but distinct data streams about newsworthy events and issues -- including activity from social media and open data sources (e.g., The Open Government Initiative) -- have given rise to a new source for and style of reporting sometimes called Data Journalism. Increasingly, news sites and information portals present visually engaging, dynamic, and interactive stories linked to the underlying data (e.g., The Guardian DataBlog). This course offers an introduction to Python programming for data analysis and visualization. Students will learn how to collect, analyze, and present various forms of data. Because numbers and their visualizations do not speak for themselves but require context, interpretation, and narrative, students will practice making effective stories from data and presenting them in blogs and other formats. No programming experience is required for this class.
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
Yujie Zhang and Yabo Vidogbena (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.
ENVS 3700 GIS: Mapping Places & Analyzing Spaces
Thomas Mckeon (R 1:45 - 4:45 pm)
This course is a hands-on introduction to the concepts and capabilities of geographic information systems (GIS). Students will develop the skills necessary for carrying out basic GIS projects and for advanced GIS coursework. The class will focus on a broad range of functional and practical applications,ranging from environmental science and planning to land use history, social demography, and public health. By the end of the course, students will be able to find, organize, map, and analyze data using both vector (i.e. drawing-based) and raster (i.e. image-based) GIS tools, while developing an appreciation for basic cartographic principles relating to map presentation. This course fulfills the spatial analysis requirement for ENVS and EASC Majors. Previous experience in the use of GIS is not required.
GAFL 5310 Statistics for Public Policy
Samantha Sangenito (MW 12 - 1:30 pm)
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.
GAFL 6400 Program Evaluation and Data Analysis
Lauren Russell (TR 10:15 - 11:15 am)
How do we know if a given program is effectively achieving its goals? How can we compare which of several programs is actually producing the greatest benefit to society? Students learn the tools needed to analyze policies, with a particular emphasis on presenting the results of quantitative analysis effectively for a nontechnical audience. By the end of this course, you will be able to 1) design quantitative and qualitative program evaluations, 2) analyze data collected for program evaluation using rigorous techniques in R, and 3) effectively present quantitative results to non-technical audiences.
MKTG 2120 Data Analysis for Marketing and Communications
Eric Eisenstein (MW 12 - 1:30 pm)
Companies are currently spending millions of dollars on data-gathering initiatives, but few are successfully capitalizing on all this data to generate revenue and increase profit. Converting data into increased business performance requires the ability to extract insights from data through analytics. This course covers the three pillars of analytics – descriptive, predictive and prescriptive – within the marketing context. Descriptive Analytics examines different types of data and how they can be visualized, ultimately helping you communicate your findings and strengthen your team’s or organization’s decision making. Predictive Analytics explores the use of data for forecasting. You will learn to utilize various tools, including regression analysis, to estimate relationships among variables and predict future behavior. Prescriptive Analytics takes you through the final step — formulating concrete recommendations. These recommendations can be directed toward a variety of marketing actions, including pricing and social-platform outreach. Students will be exposed to several methods such as linear regression, logistic regression, multinomial regression, machine learning methods (e.g., neural networks and random forests). We will learn how to employ these methods for such managerial decisions as demand forecasting, pricing, and valuing customers. Overall, you will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable insights.
MKTG 2520 Marketing Analytics
Wendy De La Rosa (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 3150 Databases for Analytics
Lorin Hitt (MW 10:15 - 11:45 am) *Half-credit course running during the second half of the semester.
Relational databases are the primary way in which business data is stored and processed. This course focuses on the analysis of data in databases and the development of databases to support analytical tasks. Over the course of the semester, students will learn the database language SQL and use this language to perform analytical tasks on existing and self-created databases. In addition, we will cover database scripting languages and extensions. The course is intended as students with little or no database background and does not presume prior computer science or coding experience. This course is nearly all hands-on coding. Students interested in more conceptual discussions of technology should consider other OIDD offerings.
OIDD 4770 / STAT 4770 Introduction to Python for Data Science
Dean Knox (MW 8:30 - 10 am / 10:15 - 11:45 am) *Full-credit versions of the course which will run the full semester.
Instructor TBA (MW 12 - 1:30 pm) *Half-credit version of the course which will be offered in both halves of the semester.
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
Giles Hooker (MW 10:15 - 11:45 am)
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
Linda Zhao (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.
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 12 - 3 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.