Taking Electives in the MS in Computational Analysis and Public Policy Program

The MS in Computational Analysis and Public Policy (MSCAPP) program is offered by the University of Chicago Harris School of Public Policy and the Department of Computer Science. In their second year, MSCAPP students have the opportunity to take electives across the University of Chicago. In this blog post, recent MSCAPP alumni share their favorite electives—and the ones that have been the most useful in their careers.

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Federico Dominguez Molina, MSCAPP’24

Data Scientist, University of Chicago Urban Labs

Favorite Elective: Software Engineering for Civic Tech: James Turk

It was a nice class to wrap the CAPP program and polish any skills where I needed more practice. James Turk is a great lecturer, and he gave us a glimpse of how a data team should work (including good practices like tests, QA, etc.). I feel it is hard to find a class that teaches you those skills before going to the workforce.

Most useful Computer Science electives: Cloud Computing: Vasilios Vasiliadis, Natural Language Processing: Chenhao Tan, Software Engineering for Civic Tech: James Turk, and Advanced Machine Learning for Public Policy: Amitabh Chaudhary
Most useful Harris Electives: Big Data and Development: Austin Wright

The first four (CS electives) sharpened my technical skills and helped me become a better coder and developer, particularly when working at projects where I need to wear several hats. I liked the Big Data and Development class because you review literature where enormous datasets are used to answer interesting questions in the public policy space, which is relevant when working in a social good role.

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Michael Rosenbaum, MSCAPP’25

Head of Methodology, Zencity

Favorite elective: Ethics of Rest: Sarah E. Fredericks

I made sure to take one elective in a discipline I had no experience in. It ended up being one of the most intellectually valuable experiences I had at Harris. I loved the opportunity to explore the ethical considerations of a topic deeply relevant for many of us at Harris who came from and will end up in governments and nonprofits: how rest can be justified when there is an opportunity to do good. The discussions and perspectives didn't just enrich my understanding of those decisions, but also broadened my understanding of the normative bases of public benefits programs, my area of focus. Looking back, I'm grateful that I made the time for something with uncertain professional benefits. It's a memory I cherish from my time at grad school

Most useful elective: Artificial Intelligence for Public Policy: Jens Ludwig

At this particular moment, there are a lot of poorly thought-out AI projects. Having a framework to evaluate how AI tools perform at a policy level has been as useful as knowing the math and engineering behind the AI tools themselves. It’s an effective link between the more technical CAPP skill set and tools from economics, and one that facilitates effective communication with both the technical decision makers who engineer AI products and the decision makers who put the AI tools into practice.

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Xiaoyue Wei, MSCAPP’25

GTM Consultant, Orcho, Entrepreneur in Residence, Reveal.ai

Favorite electives: Hacking for Defense: Will Gossin and M Todd Henderson (Booth) and Data Science Clinic (through the Data Science Institute)

I liked these classes because they both had hands-on projects with real-world stakeholders.

Most useful electives: The same two electives above^

They gave me projects experience and examples for stakeholder management and communication skills to talk about in interviews.

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Karen Yi, MSCAPP’25

Data Scientist Associate, American Institutes for Research

Favorite elective: Software Engineering for Civic Tech: James Turk 

I've used [Python web framework] Django at work since and it helped me be a lot more confident in the entire software development process. The book used in the course, A Philosophy of Software Design by John Osterhout, changed the way I look at coding and work, especially how to work with other people and establish best practices together.

Most useful electives: UIUX: André Marques

I find myself thinking about this class a lot. For example, once Professor Marques asked us what's our favorite object we own or interface we use and why, and this kind of thinking makes me look at the world for sources of inspiration in design for work. Computer science courses on Optimization and Machine Learning on Graphs also opened up new avenues of thought that are needed in anything beyond beginner-intermediate machine learning for data science.