Curriculum & Courses

Analytics and data-driven decision-making is playing an ever-larger role in modern political campaigns, think tanks, and media discussions of politics. In this foundations course, students learn about the history and evolution of data science in politics in the US and abroad, with a review of recent developments and trends as well as issues related to data governance, data privacy, and data ethics. Students are introduced to the scope of analytic methods employed across a range of relevant areas including campaigns, government, policy-making, and journalism. The course familiarizes students with a range of available tools and the ways in which they can be practically applied to politics and related fields. The course lays the groundwork for the specialized courses which will follow and helps students consider paths they may want to pursue to acquire specific knowledge and skills as they progress through the degree program.

Syllabus

Politics involves a complex interaction of competing interests.  For practitioners, it is crucial to understand how efforts are met with responses, and predicting those responses is critical to designing successful strategies.  Game theory is the formal mathematical analysis of strategic interaction across the social sciences.  This course provides a general theoretical language to the theory of games, examining the intentional thought process of rational actors in strategic environments.  Students will acquire tools for understanding the dynamics that lead to the success of a political campaign or policy-making effort.  Course topics include two-person games, dynamic games, bargaining, and signaling. Students will also examine a variety of cases.

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Field experiments have become increasingly important ways of studying the effectiveness of political interventions, be they campaign tactics for mobilizing or persuading voters, fundraising tactics for political or charitable efforts, lobbying, recruiting volunteers, or influencing administrative or judicial outcomes through direct communication.  In this course, we will discuss the logic of experimentation, its strengths and weaknesses compared to other methodologies, and the ways in which experimentation has been -- and could be -- used to investigate political phenomena. We will discuss a wide array of applications.  Students will learn how to interpret and design experiments. In order to better understand the nuances of experimental design and analysis, we will roll up our sleeves and reanalyze some of the data from the weekly readings.

Syllabus

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.

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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.

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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.

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The digital revolution has created previously unimaginable opportunities to learn about political behavior and institutions. It has also created new challenges for analyzing the massive amounts of data that are now easily accessible. Open source software has reduced barriers and inequities in coding, but it also requires different kinds of effort to employ optimally the latest innovations. Harnessing the power of political data is more critical than ever, given the threats that misinformation and alternative “facts” present to democratic forms of government.

This course will teach students both essential tools and general strategies of data science within the domain of politics. Whether students’ goals are to analyze political behavior for academic or professional purposes, successful analysis requires skills for handling a wide array of issues that stand in the way of creating knowledge and insights from data.

This course prioritizes breadth over depth in the sense that we will introduce a broad range of topics relevant for data science to develop basic skills and form a foundation that students can build on. More complete mastery of these skills will require additional engagement beyond this course.

Syllabus

Billions of dollars are raised and spent during U.S. presidential and congressional races each election cycle. Campaign expenditures play a critical role in election outcomes and political donations are used by corporations, unions, advocacy groups, and individuals to influence elected officials and public policy. Whether they are working for campaigns, advocacy groups, or consultants, political analysts need to have a sound understanding of campaign finance law and regulations, the chief strategies that contributors and recipients use to pursue their interests, and the incredibly rich data that is available to analyze and study campaign giving in the United States.

In this course, students will learn about the history and current state of campaign finance regulation, what motivates donors to give and what they may (or may not) receive in return, and how campaigns themselves fundraise and spend their billions. Students will become familiar with the ways data analytics have influenced how modern campaigns approach fundraising and the strategies used by candidates to finance a run for office. Finally, students will engage with the potential benefits and pitfalls of campaign finance reforms which, along with technological change, promise to keep political fundraising in a state of flux.

Syllabus

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.

Syllabus

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.

Syllabus

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.

This course will intensively examine some of the data analysis methods which deal with problems occurring in the use of multiple regression analysis.  It will stress computer applications and cover, as needed, data coding and data processing. Emphasis will also be placed on research design and writing research reports. 

The course assumes that students are familiar with basic statistics, inference, and multiple regression analysis and have analyzed data using computer software (e.g., any standard statistical programs on micro-computers or larger machines -- Stata, “R”, SPSS, SAS, etc.). Students will be instructed on the use of the microcomputers and the R and Stata statistical software program(s) available as freeware (R) or in the CUIT computer labs (Stata; several campus locations) or through SIPA. The lectures and required discussion section will emphasize the use of “R.” Students may use whatever computer programs they prefer for all data analysis for the course. There may be an additional fee for classroom instructional materials.

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

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.

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.

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.

The class is roughly divided into three parts: 1) programming best practices and exploratory data analysis (EDA); 2) supervised learning including regression and classification methods and 3) unsupervised learning and clustering methods. In the first part of the course we will focus writing R programs in the context of simulations, data wrangling, and EDA. Supervised learning deals with prediction problems where the outcome variable is known such as predicting a price of a house in a certain neighborhood or an outcome of a congressional race. The section on 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 genetics data.

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.

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.

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.

This course introduces students to basic spatial analytic skills. It covers introductory concepts and tools in Geographic Information Systems (GIS) and database management. As well, the course introduces students to the process of developing and writing an original spatial research project. Topics to be covered include: social theories involving space, place and reflexive relationships; social demography concepts and databases; visualizing social data using geographic information systems; exploratory spatial data analysis of social data and spatially weighted regression models, spatial regression models of social data, and space-time models. Use of open-source software (primarily the R software package) will be taught as well.

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.

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 us Python. Previous programming experience will be helpful but not requisite. Prerequisites: basic probability and statistics, basic linear algebra, and calculus.

Machine learning algorithms continue to advance in their capacity to predict outcomes and rival human judgment in a variety of settings.  This course is designed to offer insight into advanced machine learning models, including Deep Learning, Recurrent Neural Networks, Adversarial Neural Networks, Time Series models and others.  Students are expected to have familiarity with using Python, the scikit-learn package, and github.  The other half of the course will be devoted to students working in key substantive areas, where advanced machine learning will prove helpful -- areas like computer vision and images, text and natural language processing, and tabular data.  Students will be tasked to develop team projects in these areas and they will develop a public portfolio of three (or four) meaningful projects.  By the end of the course, students will be able to show their work by launching their models in live REST APIs and web-applications.

N/A

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 covered 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.

This one-semester course provides an opportunity for a student to extend or supplement their educational experience via a deep-dive into an established or novel area of research of their choice (the topic), under the guidance and supervision of a faculty member (the supervisor). An independent study course allows a student to work one-on-one with a faculty member to gain and contribute new insight into the discipline of Political Analytics.

The topic can be chosen freely by the student as long as it falls within the general realm of Political Analytics or its specific content areas in the Political Analytics curriculum, such as politics and advocacy, data analytics, campaign management, polling, and quantitative methods. The course will therefore serve the dual purpose of allowing a student to pursue their own intellectual curiosity and to make a contribution to the wider discipline of Political Analytics while also deepening their understanding of the content they acquired in other courses, by applying this material to the specific topic chosen for the Independent Study.

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.

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.

This course will examine the linkages between urban governance structures and an economically successful democratic city. We will consider the particular policy challenges that confront both developed and developing cities in the 21st century. It will be important to understand the institutional political causes of urban economic decline, the unique fiscal and legal constraints on city governments as well as the opportunities that only cities offer for democratic participation and sustainable economic growth. The course will draw on case material from primarily American cities and from other developing and developed cities around the globe. It is important to keep in mind that creative policy solutions to the problems of urban economic sustainability may be found in small towns, in rural areas, in private businesses or in other global cities. The utility of importing ideas and programs rests on a practical understanding of politics in that city or community and an effective implementation strategy. Our objective in this course is not simply to understand the challenges to governing the 21st century city but also the policies that promote effective urban governance and economic sustainability

Together we are going to learn how to plan, manage, and execute the major elements of a modern American campaign using skills that can be applied to all levels of the electoral process. What are the elements of a modern political campaign? How are those pieces executed? How do we get the people elected (or un-elected) which impacts Public Policy for decades? If you are interested in political campaigns, this is your chance to learn directly from top experts in the field about the various tools and strategies used in all aspects of American politics and campaigns today. Although this is a course focusing on practical competence, empirical political theory and relevant political science will be applied to our work. Guest lecturers, simulations, and additional materials such as videos and handouts will augment the course. When we are done, you will know what you need to do, and where you need to turn, in order to effectively organize an election campaign. The curriculum is ambitious, specialized, and task-specific. This is not a course in political science, but rather a hands-on, intensive training seminar in campaign skills. By May, you will be able to write a campaign plan, structure a fundraising effort, hire and work with consultants, plan a media campaign (both paid and unpaid), research and target a district, structure individual voter contact, use polling data, understand the utility of focus groups, write press releases, conduct advance work on behalf of your candidate, manage crises, hire and fire your staff, and tell your candidate when he or she is wrong. Our aim is to make you competent and eminently employable in the modern era of advanced campaign technology. For the purposes of this class, you will design a campaign plan for a political race. To make this more interesting (and realistic), you will be provided with information and situations throughout the semester that will require you to plan, anticipate, and adapt your campaign plan to the changing realities inherent to every campaign. The course will be co-taught by Jefrey Pollock, the Founding Partner and President of Global Strategy Group, a premier strategic research and communications firm, who has advised numerous local and national political candidates and organizations; as well as, Camille Rivera, Partner at New Deal Strategies, an experienced policy and political legislative director with a demonstrated history of working in the non-profit organization management industry.

It is strongly recommended that students have completed Quantitative Analysis before taking this course. This class will focus on properly understanding a wide range of tools and techniques involving data and analytics in campaigns. We will study evolutions and revolutions in data-driven politics, including micro-targeting, random controlled trials, and the application of insights from behavioral science, as well as more current approaches using modern statistical techniques, machine learning/AI, and natural language processing/large language models. Our primary focus will be on developments in US political and advocacy campaigns, but we will also examine the uses of these tools in development and other areas. The course is designed to provide an informative but critical overview of an area where it is often difficult to separate hype from expertise. The purpose of the course is to prepare students to understand the strengths and limitations of Big Data and analytics, and to provide concrete and practical knowledge of some of the key tools in use in campaigns and advocacy. Students will be expected to examine the use of data in practical case studies and distinguish between proper and improper uses. The course includes a track to analyze data and will spend more time giving students practical experience with current data and analytic approaches. Sample code will be provided, and students will be asked to execute and make minor revisions to the code to gain familiarity. Sample R projects will include reading and analyzing polling data, developing predictive models of voter behavior, and analyzing data from social media. Students will leave with a set of applications that can be customized to work on new data sets.

The capstone course is the culminating academic experience for students where they are afforded the opportunity to tackle a complex, real-world political analytics challenge for a sponsoring organization. Working in small teams while being mentored by a program faculty member, students synthesize, integrate, and apply core knowledge, concepts, and frameworks acquired through the program and practice the hands-on skills they have developed. Throughout the semester, student project teams interact with the sponsoring organizations as virtual consultants, scoping the problem, acquiring the data, conducting analyses, and ultimately presenting their findings and recommendations to the project sponsor.