The MS in Program Evaluation and Data Analytics (MS-PEDA) is a new degree option to train public and nonprofit sector leaders in data-driven management. The degree combines two important fields – statistical training in causal analysis, econometric techniques, and research design that serve as the foundations of evidence-based management and program evaluation, and data science courses that prepare students to work with complex datasets, effectively visualizing and communicate results, and participate in the data science ecosystem of open source software and collaboration.
Data is ubiquitous in organizations, but it rarely exists in nice, clean spreadsheets. The current graduate programs in the College of Public Service and Community Solutions all emphasize analytical models by requiring statistics courses, where students learn to apply complex modeling techniques like regression to pre-existing datasets. They spend little time, however, learning to build new datasets. In the real world, analysts spend 80 to 90 percent of their time building datasets, and 10 to 20 percent analyzing them. The current curriculum ignores 80 to 90 percent of the process. Organizations that are committed to evidence-based approaches to management and policy need employees that can quickly compile data for a specific purpose.
Employees that can generate new datasets from multiple sources using non-standard and unstructured inputs add value to organizations. These skills have traditionally been taught in computer science programs, but have recently migrated to the field of data science, a multi-disciplinary space that combines elements of data programming, statistical modeling, and graphical design to emphasize a core set of tools used to quickly acquire and analyze data, and report results in a meaningful way.
Most public policy programs are limited in that they require 4-5 conceptual courses in statistics and economics, but students receive limited training in how to apply the skills. Every concept that we teach, we would like to show applications using real-world datasets and computer code. This is because learning a programming language is like learning a musical instrument or a foreign language – the more you practice the better you get. Students need to be immersed in both the theory, application, and implementation to develop critical thinking skills around organizational performance and be facile enough with a data programming language that they can apply strategies effectively in real-world organizational contexts.
Social Science Focus
This degree is specifically tailored toward individuals with a background in social science, a commitment to public service, and no significant computer programming training (although those with backgrounds in other computer programming would benefit from training in R). The full M.S. degree prepares students to work as analysts and managers in public sector or social purpose organizations. Alternatively, those that are currently employed in the public sector but need to update their skills can decide between a certificate in Program Evaluation, or a certificate in Data Science. The degree will also serve as a source for additional methods training for graduate students across the College of Public Service and Community Solutions, or as a stepping-stone degree for someone interested in doing a PhD in public affairs or economics. We can also target undergraduate students with degrees in fields like economics, urban planning, or computer science that would like marketable data or management skills and would want to complete a master’s program in a year.
Public Service Emphasis
Public sector agencies and nonprofit organizations can benefit greatly from employees with these skills. They are difficult to hire, however, because (1) with only a few exceptions most schools of public affairs have not yet incorporated data science skills into their curricula, and (2) most data science programs are housed in schools of computer science, information systems, and business resulting in expensive degrees and established pipelines leading to private sector organizations. More so, (3) many students in the college of public service are mission-driven, and this material is taught primarily with business cases, not with social impact examples that demonstrate how idealists can use the skills. The resulting divide creates public sector managers that are not only unable to do complex data work themselves, but are also not connected to the rich data science ecosystem that can be leveraged for things like open source solutions to public problems or accessing talent through contracts and partnerships.