Bachelor’s degree in statistics, Iowa State University, Spring 2020, minors in political science, economics, mathematics, and computer science
Rural and agricultural policy, intersection of data and policy
Why an MPA?
As an undergraduate, my internships were largely focused on statistics and data science, with two of those internships working in the federal government. During one internships, I realized that I wanted to be able to do something where I could work more closely with policy and hope to have a greater impact on people’s lives.
Why the La Follette School?
When I started looking at graduate programs, I wanted to stay in the Midwest, which first put UW–Madison and the La Follette School on my radar. As I continued to do research, the La Follette School stood out as a program that was well-respected and offered great flexibility in coursework. Ultimately, when it came time to choose where to enroll, I was especially drawn to the School’s smaller cohorts, students’ experiences that I had read about in these profiles, and the funding opportunities.
I am open to many career options, but I want to be in a position that utilizes my broad skillset where I know that my work has an impact on communities, especially rural ones. I’ve considered pursuing work in state or local government agencies, nonprofit and advocacy groups, and electoral politics.
I was an undergraduate intern in the Data Science for the Public Good (DSPG) program at Iowa State University in summer 2020. I also had several internships in the federal government during summer 2018 and 2019 as a statistics and data science intern.
Primary job responsibilities
At DSPG, we worked on projects that used data science to help provide input in policy and program creation by decision makers. This involved data discovery, cleaning, visualization, and analysis to best give feedback to decision makers.
One project focused on developing a data infrastructure to measure economic mobility using the Community Capitals Framework, with a special focus on rural areas. These communities sometimes lack the wealth of data that many urban areas have, so this project served as an opportunity to look for irregular data that could be used to measure economic mobility in all types of communities. Through this project, I was able to not only broaden my quantitative skills but also learn more about regional assets and the kinds of opportunities that exist in policymaking to reduce inequities in those assets.
What experiences and skills helped you get the internship?
My data science background was particularly helpful in getting the DSPG internship. However, general interest in using data to make informed decisions that can help people was the most important factor in getting the internship.
My favorite movie is The Big Short, my favorite TV shows are The Good Place and Scrubs, and I’m an avid Iowa State Cyclones and St. Louis Cardinals fan.