Core Competency 1: American Politics
PSCI 1200/PPE 3002 Public Policy Process
This class was formerly listed as PSCI 236/PPE 312.
Parrish Bergquist (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 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
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.
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.
Darin Kapanjie (TR 10:15 am - 11:45 am, 12 pm - 1:30 pm, 3:30 pm - 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 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 (MW 10:15 am - 11:45 am, 1:45 pm - 3:15 pm, 3:30 pm - 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 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.
Rommel Regis (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 3:30 pm - 5 pm, 5:15 pm - 6:45 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.
Below is a list of approved SRDA electives on offer in Spring 2024. 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 email@example.com if you’re interested in petitioning an elective.
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.
CIS 5450 Big Data Analytics
This class was formerly listed as CIS 545.
Jacob Gardner and Ryan Marcus (TR 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.
Matthew O’Donnell (10:15 am - 11:45 am)
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.
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.
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 prerequisite, students are expected to have a foundation of data management and analysis before beginning this course.
MKTG 2120 Data and Analysis for Marketing Decisions
This class was formerly listed as MKTG 212.
Wendy De La Rosa (TR 8:30 am - 10 am, 10:15 am - 11:45 am)
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 2900 Decision Processes
This class was formerly listed as OIDD 290.
Xuanming Su (TR 8:30 am - 10 am, 10:15 am - 11:45 am)
This course is an intensive introduction to various scientific perspectives on the processes through which people make decisions. Perspectives covered include cognitive psychology of human problem-solving, judgment and choice, theories of rational judgment and decision, and the mathematical theory of games. Much of the material is technically rigorous. Prior or current enrollment in STAT 1010 or the equivalent, although not required, is strongly recommended.
PSCI 5991 Electoral Politics of the United States
Marc Meredith (T 8:30 am - 11:30 am)
Open to juniors and seniors with permission of instructor. This course will focus on both formal theoretical and empirical research about American electoral politics. The class will focus on four broad topics. The first part of the class will focus on spatial models of voting. The second part of the class will focus on electoral systems (e.g., open vs. closed primaries, ranked choice voting) with a particular emphasis on research about presidential primary elections. The third part of the class will focus on the strategies used by political campaigns and the evidence about the extent to which they mobilize and persuade voters. The fourth part of the class will focus on election administration. Most of the readings for the course will be academic books and journal articles, and will require students to consume research that contains a significant amount of mathematics and statistics. A significant amount of reading will be assigned each week (i.e., an entire academic book or three journal articles). Assessment will be based on class participation, two in-class presentations, and the development of the pre-analysis plan for a research project on American electoral politics.
SOCI 2010 Social Statistics
This class was formerly listed as SOCI 120.
Richard Patti (TR 12 pm - 1 pm)
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 4050 Statistical Computing with R
This class was formerly listed as STAT 405. This is a half-credit course.
Krishna Padmanabhan (TR 5:15 pm - 6:45 pm)
The goal of this course is to introduce students to the R programming language and related ecosystem. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.
STAT 4700 Data Analytics and Statistical Computing
This class was formerly listed as STAT 470.
Giles Hooker (10:15 am - 11:45 am, 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.
Linda Zhao (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.