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Curriculum & Courses

The Political Analytics Master of Science is a 36-credit degree program comprised of: 

  • Required core courses (12 credits)
  • Sample selective courses, divided among 3 fields (21 credits)
  • Capstone course (3 credits)

The program may be undertaken part-time and completed in six semesters. Full-time students are expected to complete the degree in three semesters.

Core courses are offered in seven-week modules, taught online to provide flexibility for working professionals.

Required core courses (12 credits): 

The 6 core courses are designed to teach a set of integral skills that focus on the following topics: 

Introduction to Political Analytics (2 credits)

This introductory course provides students with a survey of statistical and analytical methods, as well as relevant research designs. To achieve the necessary outcomes, students will be introduced to the following concepts: probability, random variables, regression analysis, correlation, t-tests, and chi-square tests.

The course will apply these techniques to the typical questions that must be addressed in campaigns for political office or in issue oriented political lobbying efforts. Problem sets and other assignments will be used to hone student skill in developing data-based campaign strategies.

The student will achieve competence in analytic techniques.

The student will learn to apply the techniques to political campaign strategy formulation and implementation.

Strategic Thinking (2 credits)

Game theory is the formal mathematical analysis of strategic interaction and it provides a general theoretical language for the analysis of interaction in many social sciences. In particular, politics involves a complex interaction of competing interests. It is crucial to understand how certain efforts are met with specific responses, and predicting those responses a priori is critical to designing successful strategies. This course examines the intentional and rational thought process that focuses on the analysis of critical factors and actors, and variables impacting the long-term success of a political campaign or policy-making effort, essential to understanding political dynamics. Students will learn about game-theoretic tools for understanding strategic interaction in the political sphere, how policy decisions are made and how they can be improved. The course will teach about basic two-person games, dynamic games, and signaling games. Students will also complete in-depth analyses of a variety of case studies focused on the design of political strategies and the resulting outcomes.

Field Experiments (2 credits)

Over the last several decades, experimental research designs have been employed with greater frequency in the study of public opinion, decision-making, political economy, and other topics in all empirical subfields of political science. Supporters of experiments assert that these methods set the standard for assessing causality. However, detractors rightly point out that causation established in a randomized controlled experiment does not necessarily translate well to causal processes in real-world situations. In this course, students will learn about the logic of experimentation, its strengths and weaknesses compared to other research methods, and the ways in which experimentation has been -- and could be -- used to investigate political behavior. Students will learn how to interpret, design, and execute experiments. Lab, field, and survey experiments will be covered. Students will learn how to address issues of non-compliance and attrition. Special attention will be devoted to field experiments or randomized trials conducted in real-world settings.

Program Evaluation (2 credits)

Political campaign managers, policymakers, lobbying firms, advocacy organizations, and other professionals operating in the political arena need to be able to distinguish effective programs from ineffectual ones. Electoral campaigns, policy-making initiatives, advocacy efforts, lobbying operations, social movement activities, and media investigations can all be assessed through a program evaluation lens, enabling improved data-based decision-making regarding whether an existing program should be continued, expanded, enhanced, or discontinued. Program evaluation techniques can also be used to assess the potential impact of new programs and to improve the effectiveness of program administration. This course focuses on (1) methods for evaluating program designs, (2) evidence collection, analysis, and interpretation, (3) frameworks for decision-making, and (4) reporting and communicating findings. Students will build upon the foundational knowledge that was established during the Strategic Thinking course and develop practical skills related to various types of program evaluation.

Survey and Polling Methodology (2 credits)

Survey research has played a pivotal role in politics for the better part of the last century, with a wide range of campaign and public policy professionals conducting surveys to gain insight into the thoughts, feelings, and opinions of the electorate and citizenry as a whole. Since the early 2000s, the use of survey experiments has become exponentially more prevalent in the political realm as a way to assess attitudes, anticipate reactions, or measure causal relationships. Recent trends point to the growing importance of the internet and social media to conduct surveys and the linkage of survey data with the wealth of publicly available personal information as well as with information on individuals’ social and economic behavior. In this course, students will learn about the strengths and weaknesses of survey research as well as limitations associated with survey design and various analytical techniques, and they will acquire concrete knowledge of practical tools used in campaigns, advocacy, and election forecasting. Students will be introduced to a set of principles for conducting survey research and analyzing survey data that are the basis for standard practice in the field. Students will be familiarized with terminology and concepts associated with survey questionnaire design, sampling, data collection and aggregation, and survey data analysis to gain insights and to test hypotheses about the nature of human and social behavior and interaction. The course will present a framework that will enable students to evaluate the influence of different sources of error on the quality of data.

Leadership (2 credits)

Successful leaders in politics, campaign management, and related professions must be able to lead change in their organizations, not only motivate and manage their teams toward a common goal. The aims that leaders seek to achieve are determined by their ability to create value, collaborate, influence, navigate uncertainty, and advance ideas, programs, and movements. In this course, students will learn about how the development of personal attributes and abilities lays the groundwork for building the core leadership competencies that are essential for high-impact management as well as changing the behavior and the culture of organizations with particular emphasis on how to successfully introduce the methods and results of analytics. Students will explore the motivations, obstacles, and interventions of change, and learn to build alliances, facilitate difficult meetings and develop a transformation plan. They will also review some of the most important academic research and business publications on change management and the implementation of analytics. The course is intended to enhance practical skills through dynamic interactions with the instructor, role-playing with classmates, and other real-world experiences.

Sample selective courses, divided among 3 fields (21 credits):

Field 1: Quantitative Methods in Political Science (9 credits): Required 2-3 courses that focus on quantitative methods within the discipline of Political Science. Courses in this field requirement reside in, but are not limited to, the Political Science Department. 

Electoral Data & Predictive Modeling (3 credits)

When investing campaign resources, including funding for advertising and field operations staff, it is critical that investment decisions be as cost-effective as possible. The use of previous behavior on voting and turnout, as well as political attitudes, is used to develop predictive models to guide investment decisions. These models are modified in real time to reflect changing political conditions. 

This course will teach students how to select data for model construction, run models, and use them to influence campaign strategies and decision-making.

The student will analyze the strategies used in ensuring effective use of campaign resources.

The student will propose and develop predictive models for investment decisions and data-based campaign strategies.

Big Data & Campaign Strategy (3 credits)

In addition to models based on voting behaviors and political attitudes, economic data, demographic data, consumer preferences, and other elements of big data can also be mined to develop campaign strategies. This form of macro-projection provides broader campaign themes than the course on predictive modeling, but when used in conjunction with more specific modeling efforts, can create more effective data-driven campaign strategies.

The student will examine and distinguish between the types of big data used in successful campaigns.

The student will utilize specific modeling techniques to develop effective data-driven campaign strategies.

Fundraising Analytics & Campaign Finance (3 credits)

All successful political campaigns require a great deal of money to succeed. This course focuses on the strategies used to raise funds. Its particular emphasis is on data from previous fund-raising efforts and determining the correlation of fundraising success. The course introduces students to the legal rules and regulatory structure governing campaign finance and the methods used to effectively raise funds within this regulatory structure.

The student will compare and contrast strategies used in securing the necessary capital to run a successful campaign.

The student will evaluate the regulatory structure and legal considerations that govern campaign finance.

The course discusses both large-donation elite and small-donation mass fundraising strategies.

Principles of Quantitative Political Research 1 (4 credits)

This course examines the basic methods of data analysis and statistics, through multivariate regression analysis, that political scientists use in quantitative research that attempts to make causal inferences about how the political world works. The same methods apply to other kinds of problems about cause and effect relationships more generally. The course will provide students with extensive experience in analyzing data and in writing (and thus reading) research papers about testable theories and hypotheses. 

Principles of Quantitative Political Research 2 (4 credits)

We will go through the second half of the book, Regression and Other Stories, by Andrew Gelman, Jennifer Hill, and Aki Vehtari (Cambridge University Press).  This is a follow-up to the course, Principles of Quantitative Political Research 1 (POLS 4710), which covers the first half of the book, including measurement, data visualization, modeling and inference, transformations, and linear regression. 

Topics covered in the course include:  

  • Applied regression: logistic regression, generalized linear models, poststratification, and design of studies.
  • Causal inference from experiments and observational studies using regression, matching, instrumental variables, discontinuity analysis, and other identification strategies.
  • Simulation, model fitting, and programming in R.
  • Key statistical problems include adjusting for differences between sample and population, adjusting for differences between treatment and control groups, extrapolating from past to future, and using observed data to learn about latent constructs of interest.
  • We focus on social science applications, including but not limited to:  public opinion and voting, economic and social behavior, and policy analysis.

Design & Analysis of Sample Surveys (4 credits)

Survey sampling is central to modern social science. Instruction in how to design, conduct, and analyze surveys, with a particular focus on public opinion surveys in the United States. 

Mathematics and Statistics for Political Science (4 credits) 

Provides students of political science with a basic set of tools needed to read, evaluate, and contribute in research areas that increasingly utilize sophisticated mathematical techniques. 

Field 2: Quantitative Methods in Social Science (6 credits): Required 2-3 courses that focus on quantitative methods within the discipline of Social Sciences. Courses in this field requirement reside in, but are not limited to, the following departments/programs: QMSS, Sociology, Statistics, Applied Analytics.  

Qualitative Research Design (3 credits) 

This course provides an in-depth examination of qualitative study designs and methods through a combination of theoretical discussion and hands-on practical experience. Topics include paradigm distinctions, theoretical perspectives, designs and methods, critique of research reports, and ethical issues in qualitative research. 

Theory & Method for Social Science (3 credits) 

This interdisciplinary course, taken in the fall semester, is a comprehensive introduction to quantitative research in the social sciences. The course focuses on foundational ideas of social science research, including strengths and weaknesses of different research designs, interpretation of data drawn from contemporary and historical contexts, and strategies for evaluating evidence. The majority of the course is comprised of two-week units examining particular research designs, with a set of scholarly articles that utilize that design. Topics include: the “science” of social science and the role of statistical models, causality and causal inference, concepts and measurement, understanding human decision making, randomization and experimental methods, observation and quasi-experimentation, sampling, survey research, and working with archival data. 

Data Mining for Social Science (3 credits)

The class is roughly divided into two parts: 1) programming best practices, exploratory data analysis (EDA), and unsupervised learning; and 2) supervised learning including regression and classification methods. In the first part of the course, we will focus on writing R programs in the context of simulations, data wrangling, and EDA. Unsupervised learning is focused on problems where the outcome variable is not known and the goal of the analysis is to find hidden structure in data such as different market segments from buying patterns or human population structure from genetic data. Supervised learning deals with prediction problems where the outcome variable is known such as predicting the price of a house in a certain neighborhood or an outcome of a congressional race. 

Natural Language Processing (3 credits)

Social scientists need to engage with natural language processing (NLP) approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide an overview of natural language processing as it is applied in a number of domains. The goal is to gain familiarity with a number of critical topics and techniques that use text as data, and then to see how those NLP techniques can be used to produce social science research and insights. This course will be hands-on, with several large-scale exercises. The course will start with an introduction to Python and associated key NLP packages and github. The course will then cover topics like language modeling; part of speech tagging; parsing; information extraction; tokenizing; topic modeling; machine translation; sentiment analysis; summarization; supervised machine learning; and hidden Markov models. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course. 

Data Visualization (3 credits)

This course is designed to the interdisciplinary and emerging field of data science. It will cover techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science to enhance the understanding of complex data. Students will be required to complete several scripting, data analysis and visualization design assignments as well as a final project. Topics include: data and image models, social and interactive visualizations, principles and designs, perception and attention, mapping and cartography, network visualization. Computational methods are emphasized and students will be expected to program in R, Javascript, D3, HTML and CSS and will be expected to submit and peer review work through Github. Students will be expected to write up the results of the project in the form of a conference paper submission.   

Machine Learning for Social Science (3 credits) 

Prerequisites: basic probability and statistics, basic linear algebra, and calculus 

This course will provide a comprehensive overview of machine learning as it is applied in a number of domains. Comparisons and contrasts will be drawn between this machine learning approach and more traditional regression-based approaches used in the social sciences. Emphasis will also be placed on opportunities to synthesize these two approaches. The course will start with an introduction to Python, the scikit-learn package and GitHub. After that, there will be some discussion of data exploration, visualization in matplotlib, preprocessing, feature engineering, variable imputation, and feature selection. Supervised learning methods will be considered, including OLS models, linear models for classification, support vector machines, decision trees and random forests, and gradient boosting. Calibration, model evaluation and strategies for dealing with imbalanced datasets, non-negative matrix factorization, and outlier detection will be considered next. This will be followed by unsupervised techniques: PCA, discriminant analysis, manifold learning, clustering, mixture models, cluster evaluation. Lastly, we will consider neural networks, convolutional neural networks for image classification and recurrent neural networks. This course will primarily use Python. Previous programming experience will be helpful but not requisite. Prerequisites: basic probability and statistics, basic linear algebra, and calculus.  

Social Network Analysis (3 credits)

The course is designed to teach students the foundations of network analysis including how to manipulate, analyze and visualize network data themselves using statistical software. We will focus on using the statistical program R for most of the work. Topics will include measures of network size, density, and tie strength, measures of network diversity, sampling issues, making ego-nets from whole networks, distance, dyads, homophily, balance and transitivity, structural holes, brokerage, measures of centrality (degree, betweenness, closeness, eigenvector, beta/Bonacich), statistical inference using network data, community detection, affiliation/bipartite networks, clustering and small worlds; positions, roles and equivalence; visualization, simulation, and network evolution over time. 

Modern Data Structures (3 credits)

This course is intended to provide a detailed tour on how to access, clean, “munge” and organize data, both big and small. (It should also give students a flavor of what would be expected of them in a typical data science interview.) Each week will have simple, moderate and complex examples in class, with code to follow. Students will then practice additional exercises at home. The end point of each project would be to get the data organized and cleaned enough so that it is in a data-frame, ready for subsequent analysis and graphing. Therefore, no analysis or visualization (beyond just basic tables and plots to make sure everything was correctly organized) will be taught; and this will free up substantial time for the “nitty-gritty” of all of this data wrangling.  

Applied Data Science (3 credits)

Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will cover using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.   

Machine Learning  (3 credits)

In recent years, machine learning techniques have made significant impact in a wide range of application areas in various industries. This course provides an introduction to machine learning concepts and algorithms, as well as the application areas. Topics will include supervised and unsupervised learning, learning theory etc. 

Data Analytics using SQL and Relational Databases (3 credits)

The role of databases in data analytics cannot be overstated; databases facilitate efficient, secure and accurate information storage and retrieval across multiple users and platforms. As a result, proficiency in database design and knowledge of SQL programming are essential skills for the modern analyst and data scientist. This course is designed to help students develop these skills.

Data hardly ever comes ready to be analyzed. In fact, in many analytics projects, –the preparation of data (be it collecting, loading, organizing, filtering, etc.) can take more than 80% of the team’s time and resources, often forcing them to rush through the analyses in order to produce results. This course will demonstrate how relational database design coupled with efficient programming can alleviate the burden of handling messy data, allowing analysts and data scientists to focus on delivering accurate, reliable and reproducible results.

While the Structured Query Language (SQL) has not changed much in the past decade, database systems and the tools that interact with them have continued to evolve. Students will be introduced to the latest programs and database connectors that allow for tight integration with Python and R as well as interactive visualization in Power BI and Tableau.

Additionally, students will be exposed to NoSQL database systems optimized for big data analytics and the techniques necessary for interacting with massive amounts of data.

This course features a final project in which students will leverage their newfound skills to tackle real-life data management scenarios by designing appropriate database schemas and demonstrating how raw data can be transformed into actionable insights. 

Python for Data Analysis (3 credits)

Python is one of the leading open source programming languages for data analysis.

This is an elective course that explores Python programming languages for data science tasks. The students in this course will learn to examine raw data with the purpose of deriving insights and drawing conclusions. Together, we will manipulate large size data sets to extract meaning and generate visualizations.

The course assumes no prior programming experience with Python. We will start by learning the fundamentals of data storage, input and output, control structures, functions, sequence and lists, file I/O, and standard library classes. We will then move on to learning Object-Oriented Programming with Python: encapsulation, inheritance and polymorphism.

To explore the Python data analysis platform, we will focus on IPython (Interactive Python) and Jupyter Notebook. IPython is an enhanced interactive Python terminal specifically designed for scientific computing and data analysis; Jupyter Notebook is a graphical interface that combines code, text, equations, and plots in a unified interactive environment.

Students will learn to work with widely-used libraries, such as pandas for data analysis and statistics; NumPy for its practical multi-dimensional array object; and MatPlotLib for graphical plotting. We will use these libraries to load, explore and visualize real-world datasets. 

Field 3:  Topics in Politics (6 credits): Required 2-3 courses that focus on Topics in Politics. Courses in this field requirement reside in, but are not limited to, the following departments/programs: Political Science, Sociology, Statistics, Applied Analytics.

Elections (4 credits)

This course examines the electoral behavior of the American public and the interpretation of election outcomes. 

State and Local Politics (4 credits)

Governing the 21st Century City (3 credits)

Political Psychology (4 credits)

Prerequisites: the instructor's permission. The survey course on political psychology is organized around three main themes. The first is social influence and intrinsic predispositions: obedience, conformity, social pressure, authoritarianism, and personality traits. The second theme concerns the manner in which people interpret new information about politics and use it to update their beliefs and evaluations. This section invites discussion of topics such as: To what extent and in what ways do media and politicians manipulate citizens? Can and do voters use information shortcuts to compensate for their lack of direct information about policies? The third theme is the meaning, measurement, and expression of ideology and prejudice. 

Field Seminar on Political Behavior (4 credits)

Urban Politics and Policy (3 credits)

All public policy occurs within a political context. The purpose of this seminar is to examine the politics of America's large cities. While we rely on case material from American cities the theoretical and applied problems we consider are relevant to understanding public policy in any global city. Cities are not legal entities defined in the American Constitution. Yet, historically they have developed a politics and policymaking process that at once seems archetypically American and strangely foreign We will consider whether America's traditional institutions of representation work for urban America; how the city functions within our federal system; and whether neighborhood democracy is a meaningful construct. We will also consider the impact of politics on urban policymaking. Can cities solve the myriad problems of their populations under existing institutional arrangements? Are cities really rebounding economically or does a crisis remain in communities beyond the resurgence in many downtown business districts? Do the economic and social factors which impact urban politics and policy delimit the city's capacity to find and implement solutions to their problems? Finally, can urban politics be structured to make cities places where working and middle class people choose to live and work and businesses choose to locate; the ultimate test of their viability in the twenty first century.

Political Communications (3 credits)

This course is designed either for students who wish to embark on or further careers in politics and for those interested in exploring the dynamic field of political communication. Three themes anchor the course material: 1.) strategic communication, or deliberate and goal-oriented communication, which enables professionals to analyze and execute political strategy; 2.) message, which enables the crafting and critique of more or less effective political communication; and 3,) research, which political professionals use to formulate, shift and optimize their strategies. 

Capstone course (3 credits):

The 3-credit capstone course will be a group project advised by a faculty member for a client. The project would be a piece of analytic work requested by a political campaign or a political strategy for an NGO. An effort would be made to diversify the client’s political party, ideology, location, and level of government. 


To accommodate working professionals, most classes are scheduled from 6:10–8:00 p.m. or 8:10–10:00 p.m.

The University reserves the right to withdraw or modify the courses of instruction or to change the instructors as may become necessary