Course Descriptions

Everything from the Affordable Care Act to the Mayor’s Rebuild Initiative here in Philadelphia could not be implemented by government without strong and vital partnerships with non-profits and the private sector. These collaborations provide an opportunity to help people, impact and change policy, improve outcomes, and multiply the impact that non-profit and private sector organizations can have. The course will help graduate (and advanced undergraduate) students not only understand the theory, policy, and practice of these collaborations but also learn how they actually happen. Students will also learn the characteristics of these three sectors, their roles and contributions, and competitive forces that are often at work in the collaborative process. Topics for discussion will include attitudes and expectations in the public sector, the ingredients of effective partnerships, and effective communication strategies with elected and appointed officials.

The course will be conducted on a seminar basis. Graduate students are expected to take an active part in shaping the discussion. Students will be expected to rotate leadership for the class discussions and to supplement course materials with independent study of relevant magazine and newspaper articles. Course grades are assigned as follows: 20 percent for class participation, 15 percent for an in-class written exam, 30 percent for a group presentation and write up of a case study, and 35 percent for a final project. High quality written work and accurate citations is an expectation in all assignments.

Data for Equitable Justice Lab gives SP2 Masters students an opportunity to analyze some of today’s most important social issues through data and, with faculty support, create a product for audiences well beyond our classrooms and campus.

With guidance from the lab faculty, students develop a project – either individually or as part of a team – to examine a contemporary social policy or political issue through or on data or digital technology. Through these projects students will produce an op-ed, blog post, podcast, academic article, short film, or other product of their choosing that creates or contributes to contemporary discourse.

This course familiarizes students with no prior programming experience with the core concepts of programming and the practice of software development for data-intensive applications in industry and government. After this course, students will be comfortable (1) writing code to save and load from files and spreadsheets into basic data structures like strings, lists, and maps; (2) manipulating data with code to perform tasks like generating aggregate statistics and filtering data into subsets; (3) effectively communicating findings from interactive, exploratory programming with others; and (4) working with technical teams, using best practices of software development when building line-of-business applications.

Topics covered include:

  • Programming
    • Basics: Hardware vs software, local vs. cloud, programming languages
    • Flow control: Sequential, conditional, and loop statements
    • Data structures: Strings, lists, tuples, sets, and dictionaries
    • Structure in programming: functions, methods, objects, classes
    • Recursion
    • Testing and QA – manual, unit testing, functional testing
    • Computational Complexity – linear, polynomial, exponential time algorithms
  • Data and Communication
    • Working with text files, spreadsheets, databases
    • Working with a REPL
    • Introductory analysis – summary statistics, significance tests, regressions
    • Visualization with plots and tables
    • Heuristics for presenting and describing data on teams
  • Teamwork
    • Waterfall and agile project management
    • Software engineering, product management, user experience research
    • Organization of software teams: backend, frontend, ops, QA
    • Data scientists and researchers in organizations
    • Collaboration: teams, stakeholders, vendors, customers, end users
    • Regulation, compliance, and privacy in the context of sensitive datasets
    • Open source and proprietary software

This course prepares students with no background in machine learning or data science to use tools from those fields effectively in applied contexts. Using GUI-based software – or optionally, by programming with libraries – students will build skills including (1) feature representations of spreadsheet-based or text datasets; (2) training classification and regression models for prediction tasks; (3) evaluation of machine learning model accuracy and error analysis; and (4) reasoning about predictive models and making tradeoffs like bias vs. variance, granularity and annotation complexity in labeled training data, and the ethical application of predictive modeling to human-centered data.

Topics covered include

  • Input and output
    • Working with files and databases
    • Working with a REPL
    • UIs for interacting with data
  • Problem definition in machine learning
    • Problem definition: classification, regression, reinforcement, structured output
    • Defining labels, ground truth, and measures of inter-rater reliability
    • Performance metrics and evaluation: Precision and recall, sensitivity and specificity, correlation, Kappa
    • Training and testing data, cross-validation, and evaluation
  • Basic data science methods
    • Regression tasks: Linear, multivariate regression, basic hierarchical models
    • Classification tasks: Binary and Logistic regression
    • Conditional decision-making: decision trees and nonlinear regression
    • Unsupervised methods: k-means clustering)
  • Machine learning methods
    • Feature space definition, feature extraction and dimensionality reduction
    • Classification algorithms: logistic regression, decision trees, SVMs, k-Nearest-Neighbors, neural networks
    • Confusion matrices, error analysis, feature selection, optimization, overfitting
  • Reasoning about machine learning
    • Tradeoffs: Accuracy, interpretability, and fairness of models
    • Training data maintenance and semantic drift
    • Active learning and human-in-the-loop data annotation methods
    • Machine learning in decision-making processes

How does the design of everyday objects and systems in our social word —from the workplace to the civic sector—produce variation in our political participation to promote or stifle the collective project of social justice? Systemic injustice expresses itself in everything from software interface designs to paper passport applications. Using these objects and others, this course focuses on the ways in which power operates through and within aesthetics to create and enforce difference and produce the inequalities that demand a collective reimagining of our world. What might we learn from these “aesthetic assemblages” of power and difference, and their manifestations in current social policy?

In this course, we will work with case studies from a range of politically urgent topics—mass incarceration, immigration reform, healthcare inequity—through the lens of critical theories and pedagogies that center the lives of those communities most impacted by discriminatory social policy. Students will learn to apply the thinking of scholars such as Fred Moten & Stefano Harney, Dean Spade, and Mel Chen towards their own social justice-informed approaches to social policy and practice. Through independent study projects, students will explore their own unique areas of interest beyond the scope of this course to rethink how critical theory can shape and be shaped by on-the-ground, everyday practices.

Policy analysis requires an understanding of social problems/social issues and the processes by which policy is developed and implemented. Critical skills in many policy frameworks include: problem definition and analysis, review of relevant research, identification of possible actions, implementation and evaluation, and fiscal analysis. Competency in written and oral communication is also essential. To develop these and related skills, this course utilizes as a base a dynamic social problem analysis framework that addresses issues of equity, equality and adequacy. It also examines multiple theoretical and analytical perspectives. Through the review of contemporary and historical social policy debates and provisions, selected case examples and policy briefs, this course provides students with an understanding of the policy roles of the legislative and executive branches of government, including goal setting, policy rulemaking and enactment, allocation of resources, financing, regulation, and implementation. The policy process at state and local levels of government will also be addressed. The primary focus is on U.S. policy although global policies will be discussed when relevant.

Research & Evaluation Design introduces social research methods in the context of social policy and program evaluation. The course provides a conceptual and practical understanding in the design of experimental, quasi-experimental, and non-experimental research and in the application of quantitative and qualitative methods. Students learn about the application of the research process and skills in all phases of assessing a social policy and developing a social program, including needs assessment, implementation analysis, and evaluation of policy or program effectiveness. Students learn to be critical and informed consumers of research and to apply guidelines of research ethics in social policy settings.

What do these numbers mean? Do they confirm my theory? And what is a theory anyway? In this class, we will be exploring simple theories and how to test them using data. We will also look into how data can give us clues to formulate our theories. We will discuss how to plot data
to understand its contents and potential problems. Once we understand what is in the data, we can start testing some simple theories. For example, can we say that more educated people earn more than less educated people? And how confident can we be about this statement? Even if more educated people do earn more than less educated people, does this mean that increasing education will be causing people to earn more? Or is it simply that more educated people are smarter to begin with? We will see how data can allow us to solve this kind of question and advise policy makers on the benefits of increased education.

This course introduces students to the basics of the American legal system, focusing on the interplay between litigation and social policy. Students will learn how law, and particularly case law, is made, how to read case law and evaluate precedent, legal reasoning and argument. This course will utilize various teaching methods including introduction to the “Socratic” lecturing method which is frequently utilized in the study of law. Students will study the structure of court systems at both state and federal levels as well as the litigation process and the role of law and courts in shaping and addressing social policy issues. Students will also learn the basics of several areas of substantive law, with an eye toward consideration of how that law has been, and can be, used to effect social change.

The focus of Capstone I: Policy Communications (.5 CU) is three-fold:

  1. To enhance student integration of the theory and practice of social policy analysis;
  2. To enhance the student’s competencies in the written and oral communication processes and procedures necessary for the policy world; and
  3. To ensure basic knowledge about federal budget processes, stakeholder roles, and inter-organizational collaboration.

Capstone II: Policy Internship (.5 CU) consists of an intensive, multi-week policy internship that is selected through a consultative process involving the student, internship coordinator, advisor, and mentors/supervisors at potential sites.

The volume and complexity of data continues to increase in the world around us, including science, business, medicine, social media and everyday human activity. This course aims to expose students to visual representation methods and techniques that increase the understanding of complex data. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making. In this course, students will learn about the fundamentals of perception, the theory of visualization, and good design practices for visualization.

The course will also provide hands-on experience on the process of data communication, from initial data analysis, to identifying appropriate visualization techniques, to crafting informative visualizations.

Economics allows us to determine the costs and benefits of social policies like cash benefits, unemployment insurance, health insurance, pensions, education, etc. Policies typically affect the behavior of agents like individuals, families and firms, and we have to take these reactions into account when analyzing policy. Economics allows us to predict how policy is likely to affect behavior by understanding how the policy changes the agents’ decisions, and what collective outcomes these myriad individual decisions bring about.  For example, unemployment insurance allows individuals to sustain themselves and their families when they are out of a job. At the same time, unemployment insurance provides an incentive for people to search less
hard for a job, and this ultimately increases the time they spend unemployed. When all of the unemployed behave this way, the unemployment rate in the economy tends to increase. Policy makers have to take these phenomena into account in order to design a good unemployment insurance system.

The history of the relationship between race and technology has long been fraught. On the one hand, the sociopolitical formation of race constituted black and brown bodies in juxtaposition to the logics of reason that the instruments of post-Enlightenment technicity were built. On the other hand, as Wendy Chun argues, the discursive formation of race was a technology in and of itself that was designed to hierarchize and differentiate bodies as well as to make black and brown bodies extracted technologies for labor and Capital. This seminar will explore this deeply enmeshed history between race and technology by engaging text in the history of science and philosophy, critical theories of technology, cybernetics, and critical theories of difference. These texts will range in topics from the transparent subject to surveillance studies to algorithmic bias to the speculative fiction of Afrofuturism. The text will include both scholarly written products as well as media and popular culture. Students will learn about the history of philosophy and technology in relation to race and the (em)body as well as how to examine for speculative futures.

While social mechanisms of power might be kept out of sight, their productive capacities are generative of volumes of material. This course focuses on the material traces of power to map how bureaucracy, at all scales and registers, creates and enforces difference as a power differential. Specifically, we will explore how power expresses itself aesthetically in bureaucratic processes as in, for example, the organization of spreadsheets, the distribution of administrative power via forms and chains of command, and software design.

Course materials, assignments, and lectures will triangulate theory, evidence, and policy as a way of grounding parallel inquiries into the ethics of these assemblages and their manifestations. The final three weeks of term have been reserved for group reflection and synthesis. Students are able to introduce new areas of exploration at this time specific to their interests.

This course will observe alternate modes of contesting power asymmetries in society and culture. In particular we will observe how projects such as those organized by the Black Panther Party, Brown Berets, and the Farm Workers Movement reveal something to us about how policies are unjustly received on the ground, and how para-governmental projects can provide a pathway for understanding how to create more just societies, policies, and alliances. We will also explore more contemporary practices of social justice through art practices that have also specifically targeted policy, such as those carried out by the Carrot Workers Collective, Critical Art Ensemble, Natalie Jeremijenko, and others. The aim of the course will be to use these practices to try to develop an ethics divorced from norms that could then productively be applied to policy making.

The relentless focus on the being of health inequity often overshadows the becoming of health inequity. Each drip of social injustice pools into a confrontation that disproportionately affects the health and healthcare of the socially disadvantaged groups. This course navigates health policymaking through a sociohistoric lens and grapples with contemporary perspectives in health equity. We explore the theoretical frameworks that best informs the existence of health inequity along with the practices that eliminate health inequity. Students will have the opportunity to learn how to effectively communicate evidence-based strategies in both policy and academic grant formats. While generally structured as a seminar, this course extends the walls of the classroom and encourages students to confront real-life health policy issues while engaging local, state, and federal health policy influencers. Students will spend time in the robust archives and cutting-edge medical facilities at Penn to best hone their policymaking voice.

With the advent of digital technologies and the increasing power of computational analytics, the proliferation and ubiquity of data production has increased at exponential rates enabling new possibilities for social analysis. This course will examine the emergence of democratizing data – the movement to make government and other data more widely or publicly available and its potential enabling for democratic possibilities. The types of data being made available, through various analytic systems, and the ways in which their accessibility and inaccessibility is contributing to reconfigured power relations, will be described. The paradigmatic tensions and shifts that have emerged in the debates on “Big Data,” such as deductive versus inductive reasoning and the challenges posed to statistical sampling theory, will be interrogated. The appropriation of machine learning and predictive analytic algorithms for social analysis will be critically explored. Issues related to the ethical and legal use of administrative data, particularly data related to patient, client, student, and taxpayer information will be considered, as well as from internet-based sources including social media. Potential solutions to data security challenges will be additionally considered.

Methods for web-scraping of data, analysis of web traffic data, and the use of social networking data in the modeling of social phenomena and public opinion will be examined. Students will learn how to make results accessible to non-technical audiences via data visualization tools, such as web-based data dashboards and web-based maps. These topics will be discussed for the analysis of health, education, and social policy as well as their implications for questions pertaining to race, gender, class, sexuality, dis/abilities, age, and youth culture. This course will develop students’ knowledge of computational and data analytics and its applications for social policy analysis.

Gender and Social Policy develops an advanced understanding of social policies through a focus on social issues and conditions through the lens of gender, economic, and critical theory. The specialized focus on gender and social policy provides students with the opportunity to develop more specialized knowledge about how market dynamics and government policies respond to the needs and risks faced by women. Specific emphasis is placed on utilizing theoretical frameworks to evaluate the intersection between social policy, history, and social science in relationship to gender issues. Students are also expected to conduct a policy analysis that includes an evaluation of how current and former social movements surrounding gender issues shaped their policy of interest.

In this course we will explore ways to provide women with practical, “real world” skills that will enable them to achieve meaningful political and advocacy participation. The course is designed to give you the theoretical background and tools to put together a meaningful international training such as those sponsored by Women’s Campaign International. The course will also focus on political and community organizing, communications, fundraising, advocacy and media experience, which will aid women politically, economically and civically in the life of their communities. Students will not only gain experience from working with Women’s Campaign International’s trainers, but will also learn how to develop training strategies for specific countries – addressing the particular challenges within countries that women face as they determine their political and economic involvement in emerging democracies.

Social constructions of “difference” permeate the institutions, spaces, and assumptions of our society. These social constructions include but are not limited to the racialized, gendered, sexed, classed, and dis/abled constructions of the body. By leaning on postmodern thinkers such as Iris Marion Young, Pierre Bourdieu, Judith Butler, Jacques Derrida, Ernesto Laclau, and Michel Foucault, this seminar course will begin by engaging the questions of what is “difference” and how is “difference” discursively constructed and reproduced in society. Using a postmodern lens, the remainder two-thirds of the course will engage various social science text that deal with the varieties of “difference” (i.e. race, gender, class, sexuality) and the explicit and/or implicit policy implications of these works. Thus, we will critically engage policies such as welfare, affirmative action, economic policies of taxation, and same-gender marriage among others. The underlying questions throughout the course will be to what extent does social policy enable the possibilities of freedom, justice, and democracy for the “Other”, the deviant, the abject, the marginalized, those of assumed “difference”? And, to what extent does policy constrain those possibilities at the same time?

Cuba represents one of the world’s long-standing institutionalized revolutions whose narrative and policies have changed from a strong nationalism yearning for Independence, to an alignment with communism’s ideology and modus operandi, to a nostalgic, post-Soviet Union “socialism” ruled by a binary, state-controlled capitalism. In addition to the myriad of social and political changes affecting the island, the transition of leadership from Fidel Castro to his brother, Raúl, and the death of the former in 2016, has put into question the theoretical pillars of the Revolution, thus undermining its initial legitimacy. This course is designed to provide students with the critical and analytical tools to dissect Cuban revolutionary politics, policies, and identity mutations within the island’s historical trajectory. We will begin by analyzing the notion of Independence – upon which Castro relied to gather massive support – in the context of the 60’s debates on decolonization and underdevelopment. In addition, we will delve into the theoretical foundations of the Revolution focusing, among other texts, on the literature by Cuba’s “founding father” José Martí, who deeply influenced the Spanish-American war (1898)’s outcomes as well as Fidel Castro’s vision for Cuba. Throughout the course, students will also have the opportunity to critically read and discuss main Cuban social policies such as its famous Literacy Campaign, and other Education, Housing, Cultural, Health, and Immigration policies, as well as the island’s complex relationship with technological development and communications. Finally, we will study identity and race dynamics, which are inextricably embedded in Cuba’s political landscape.

This course will begin with several introductory sessions at the University of Pennsylvania, followed by ten class meetings during a two-week stay in Havana, Cuba. Once in the island, students will visit key historical and cultural sites, and engage in conversations with distinguished Cuban scholars and cultural critics. Lastly, students are required to develop a research project on a particular Cuban social policy and produce a final paper.

This course deals with the underlying assumptions and applications of the general linear model with social science, education, and social policy related questions/data. The first half of the course begins by covering simple linear regression and the assumptions of the general linear model, assumption diagnostics, consequences of violation, and how to correct for violated assumptions. This will also include methods of incomplete case analysis (i.e. missing data analysis). Then various aspects of regression analysis with multiple independent variables will be covered including categorical explanatory variables (e.g. to estimate group differences), interaction effects, mediating effects (e.g. to estimate the indirect effect of social processes), and non-linear effects. The course will then cover some of the applications of the general(ized) linear model including logistic regression, some elements of path modeling (structural equation modeling), multilevel analysis (hierarchical linear modeling ), and longitudinal modeling (growth modeling). The course will be taught using SAS, but students are welcome to use any statistical package of comfort. Pre-requisite: Introductory Graduate Statistics.

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