A Summer of Data Science Homework?

Communications office student writer Majestic Terhune '21 is spending her free time this summer taking an online data science course. See what the literature and world politics major has to say about her experience.

At the end of my work day, I do what any college student would at the end of a warm, clear summer day: I take a walk, make dinner, and get started on homework.

It’s true. I spend most evenings completing exercises for my online data science course, which Hamilton offers through the Liberal Arts Collaborative for Digital Innovation (LACOL). The course includes 11 Hamilton students and dozens more from other colleges, such as Swarthmore and Williams, and is taught by professors throughout the LACOL system.

About Majestic Terhune ’21

Majors: Literature and World Politics

Hometown: Indianola, Iowa

HS: Phillips Exeter Academy

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Largely asynchronous, I do most of my work through online programs like DataCamp and RStudio. So far, most of my learning has focused on generating statistics from datasets, creating graphs, and generally thinking through the best ways to analyze and communicate data.

Almost all the course exercises focus on R, a programming language often used for statistics. For my most recent assignment, I used R to create 3D graphs about voting behavior in the 2016 election, mapping shifts in party alignment against unemployment and shifts in alcohol mortality by county. Likewise, for my final group project, which includes Sara Shedroff ’23, I will conduct analyses on lead levels taken throughout Flint, Mich.

I learned about the course from Professor of Geosciences Dave Bailey last semester during my Earth Resources and the Environment class. Noting the importance of data scientists, Bailey encouraged me and the other students to reach out to Hamilton’s LACOL liaison, Assistant Professor of Geosciences Cat Beck.

As a rising senior majoring in literature and world politics, taking an introductory geoscience course was as unusual for me as taking a summer data science course, but I suppose I decided to take both classes for the same reason: I like learning.

Both geoscience and data science were relatively unfamiliar to me, and I knew studying them would provide an opportunity to educate and challenge myself. Learning more about data science has been what I expected it to be: informative and difficult, but mostly empowering. I like gaining skills that help me explore and understand the world around me. And though I am sure I can use my new statistics toolkit to help me with literary and political research later on, I currently appreciate my newfound ability to evaluate and question the statistical information I encounter in everyday life — even if it’s just in relatives’ Facebook posts.

Though taking the course means I spend my free time coding, I like coding, and I like knowing that I can use it to expand my comprehension of a variety of subjects. I am ultimately eager to use data science after the course ends, whether that be inside or outside of class.

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