Data Science Student interested in making data meaningful through analytics, sports, and performance.
I'm a Data Science major at Indiana University with a minor in Statistics and Sports Marketing and Management. I'm deeply interested in the intersection of analytics, sports, and performance - and in making data meaningful, using it to inform strategies that make teams and organizations stronger.
My coursework and projects have given me hands-on experience in Python, R, and data visualization. Outside the classroom, I work as a manager at IU's campus box office, where I've picked up real experience in operations, customer service, and leading a team. I'm also active in the Women in Technology Club and IU Women's Basketball Club, stay involved in my sorority, Alpha Delta Pi, and volunteer regularly at the Bloomington Animal Shelter. When I'm not working or in class, you'll find me hiking, playing basketball, going on adventures with friends, or mentoring young athletes through camps.
I'm eager to bring my skills into a collaborative, forward-thinking environment where I can keep learning and contribute to meaningful projects.
From applying machine learning models in coursework to working in fast-paced team environments, I've developed both technical expertise and strong interpersonal skills.
Recent projects, group and individual, spanning data analysis to creative collaboration. Click any card to explore the deliverables and my specific contributions.
Analyzed recruitment data to optimize matching of potential new members with current members, guiding strategic membership decisions during a three-week recruitment period.
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Examined how Caitlin Clark's presence influences WNBA attendance and fan engagement via web scraping, social trends, and linear regression.
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Used Python and machine learning to classify beer styles by flavor attributes, building a decision tree with 59% overall accuracy.
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Led a team through a creative athlete-branding pitch modeled after Shark Tank - recognized for the best presentation in our section.
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A semester of coursework on how data gets represented and processed - signals, images, time series, graphs, and core machine learning models.
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Led a team of five using correlation and multiple regression on real NFL data to test whether Combine metrics actually predict rookie performance.
Read more →Short reads on data, sports analytics, and what I'm learning along the way.
What a class on human and animal cognition is teaching me about how modern AI models learn, predict, and mirror our own brains.
Read article →What I learned interning in marketing analytics for a company that sells local, place-dependent leagues - ad fatigue, regional attribution, and the real math behind profitability.
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