PORESFALL 2023
CORE CLASSES
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 10:15 am - 11:15 am)
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. You are encouraged (but certainly not required) to register for both this course and PSCI 1801 at the same time, as the courses cover distinct, but complimentary material.
PSCI 1801 Statistical Methods in Political Science
This class was formerly listed as PSCI 338.
Marc Trussler (MW 12 pm - 1:30 pm)
The goal of this class is to expose students to the process by which quantitative political science research is conducted. The class will take us down three separate, but related tracks. Track one will teach some basic tools necessary to conduct quantitative political science research. Topics covered will include descriptive statistics, sampling, probability and statistical theory, and regression analysis. However, conducting empirical research requires that we actually be able to apply these tools. Thus, track two will teach us how to implement some of these basic tools using the computer program R. However, if we want to implement these tools, we also need to be able to develop hypotheses that we want to test. Thus, track three will teach some basics in research design. Topics will include independent and dependent variables, generating testable hypotheses, and issues in causality. You are encouraged to register for both this course and PSCI 1800 at the same time, as the courses cover distinct but complementary material. But there are no prerequisites nor is registering for PSCI 1800 necessary, in order to take this course. The class satisfies the College of Arts and Sciences Quantitative Data Analysis (QDA) requirement.
ECON 2300 Statistics for Economists
This class was formerly listed as ECON 103.
Wayne Gao (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.
MW 8:30 am - 9:30 am, MW 10:15 am - 11:45 am, MW 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.
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.
Elizabeth Ajazi (TR 12 pm - 1 pm, TR 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 4300 Probability
This class was formerly listed as STAT 430.
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 (TR 10:15 am - 11:45 am)
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 Survey Research and Design
This class was formerly listed as PSCI 332.
Will Marble (TR 3:30 pm - 5 pm)
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
PSCI 1207 / URBS 3200 / GAFL 5090 Who Gets Elected and Why? The Science of Politics
This class was formerly listed as PSCI 320 / URBS 320 / GAFL 509.
Ed Rendell, Elizabeth Burdett (M 5:15 pm - 8:15 pm)
What does it take to get elected to office? What are the key elements of a successful political campaign? What are the crucial issues guiding campaigns and elections in the U.S. at the beginning of the 21st century? This class will address the process and results of electoral politics at the local, state, and federal levels. Course participants will study the stages and strategies of running for public office and will discuss the various influences on getting elected, including: Campaign finance and fundraising, demographics, polling, the media, staffing, economics, and party organization. Each week we will be joined by guest speakers who are nationally recognized professionals, with expertise in different areas of the campaign and election process. Students will also analyze campaign case studies and the career of the instructor himself. Edward G. Rendell is the former Mayor of Philadelphia, former Chair of the Democratic National Committee, and former Governor of Pennsylvania.
COMM 2100 Quantitative Research Methods in Communication
This class was formerly listed as COMM 210.
John Jemmott (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 2260 Introduction to Political Communication
This class was formerly listed as COMM 226.
Kathleen Jamieson, Shawn Patterson (MW 1:45 pm - 3:15 pm)
This course is an introduction to the field of political communication and conceptual approaches to analyzing communication in various forms, including advertising, speech making, campaign debates, and candidates' and office-holders' uses of social media and efforts to frame news. The focus of this course is on the interplay in the U.S. between media and politics. The course includes a history of campaign practices from the 1952 presidential contest through the election of 2020.
COMM 3180 Stories from Data: Introduction to Programming for Data Journalism
This class was formerly listed as COMM 318.
Matthew O’Donnell (TR 3:30 pm - 5 pm)
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 I
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 examining 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).
CIS 5450 Computational Data Exploration
This class was formerly listed as CIS 545.
Zachary Ives (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).
STAT 4750 Sample Survey Design
This class was formerly listed as STAT 475.
Elaine Zanutto (T 5:15 pm - 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.
OIDD 2900 Decision Processes
This class was formerly listed as OIDD 290.
Xuanming Su (TR 8:30 am - 10 am, TR 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 101 or the equivalent, although not required, is strongly recommended.
ECON 2310 Econometric Methods and Models
This class was formerly listed as ECON 104.
Xu Cheng (MW 1:45 pm - 3:15 pm)
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.