Not too long ago, we had project-wide midpoint check-ins where lab members came together in small groups to discuss our experiences so far in the Library as Research Lab project. Many of the topics had to do with how the skills learned in this project differ from – or augment – our experiences in the classroom at the School of Information. Being a project member who hails from the School of Information Data Science track, I had a lot of thoughts about the value of being an interdisciplinary student that I wanted to continue unpacking after the focus group. Not only has this program been deeply influential in shaping my academic trajectory at UMSI, but it has influenced how I think about my future career as well. More than just a research project, the Library as Research Lab program has helped me better articulate who I want to be as a data scientist.
I originally applied to this project because, although I’m a data science student, I come from a social science background and knew that I wanted to go into a career that somehow blended data science with research and education. When I heard about the Library Assessment of Student Learning Lab, I realized that it would be a perfect fit for my time at UMSI. Not only do I get to work with both qualitative and quantitative data, but I also get to ask questions about how student learning takes place in the context of various library programs.
As part of this program I’ve had the chance to work with data differently than I do in most of my coursework. While many of the courses in the UMSI data science track certainly push students to think through how to develop meaningful research questions and research methodologies, there’s something special about being in the Library as Research Lab program where thinking critically about research methodology is part of the daily texture of our conversations. It can be so easy to assume that any time we answer a question using statistics, or “data science,” or any other method that wraps the process of question-answering in numbers, the answer is somehow “untouchable.” Because it’s based in math, which seems so objective, it must be correct and unbiased! In reality, it’s actually pretty easy to throw numbers together and come up with a result that, on the surface, seems to answer whatever question we happen to be asking. It’s much more difficult to think critically about where data comes from, what inferences can actually be drawn from data, and whether we’re asking questions in a way that’s measurable and meaningful.
Participating in this project has slowed me down in the best of ways. Instead of rushing forward with a solution, I take time to think about what it means to answer a question in a particular way. Are the data reliable? Does the methodology make sense? Are there any ethics questions that I need to address before touching a dataset? Overall, this program has made me a much stronger data scientist.
Elizabeth Hanley graduated from Kalamazoo College with a B.S. in Psychology. With an interest in exploring how big data and technology can improve education, she is now pursuing an M.S.I. with a focus on Data Science at the University of Michigan School of Information.