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Welcome to my Data Analysis Projects page, where I combine creative problem-solving with rigorous quantitative methods. From using Python to examine U.S. museum data—revealing insights on funding and audience trends—to applying ordinary least squares (OLS) models in an A/B testing environment to explore how emotions affect video comprehension, each project demonstrates the transformative power of data-driven inquiry in diverse contexts.
Museums in the U.S. (Python Data Analysis Project)
I developed a Python-based tool to analyze and visualize museum data from the Institute of Museum and Library Services (IMLS). By processing two CSV files—one for overall income data and another for category details—the program provides a comprehensive overview of museums in each state, including total income and category distributions. Users can enter commands to generate bar charts (e.g., number of museums or total income) or retrieve pie charts of specific states’ categories. Built with NumPy and Matplotlib, this interactive tool supports more informed decision-making for art administrators, curators, and anyone interested in museum planning.
Emotion and the Ability to Understand the Video (A/B Testing)
I conducted an experiment using Ordinary Least Squares (OLS) models to examine whether machine-detected or self-reported emotions—such as happiness, anger, or surprise—significantly affect quiz accuracy for an educational video. Interestingly, fear emerged as the only emotion consistently tied to lower comprehension, suggesting that intense negative reactions can hinder learning. Other emotions showed minimal or no statistically significant effect. By comparing AI-based facial recognition with participants’ own reports, the study also underscored the challenges of accurately measuring and interpreting emotional data. This research offers insights into how emotional states may shape learning outcomes and how different methods of data collection can influence our understanding of emotion’s role in cognition.
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