BMI as an Indicator of Robust Health

This was a final project that was completed as part of my Master’s course in Experimental Design in Data Science. This course explored best principles in human-centered data science experimentation, seeking to understand the relationship between research questions, hypothesis testing and experimental design from a quantitative perspective. The project was structured to explore some of the experimentation methods taught throughout, and was reviewed by graders and peers one time prior to final submission.

This was a solo project, where I was tasked with building an experimental report based on variables of my choosing within a large diabetes dataset. This dataset needed to be cleaned, visualized for preliminary exploration, and tested on through the lens of my data-backed hypothesis. The tests included Multiple Linear Regression, ANOVA, ANCOVA, and Post-hoc testing. I used Python to execute these tasks, and included the critical outcomes of them in the final report.

The two deliverables for this project were the report and the Python notebook. You can find them both here -