Research & Sports Analytics
Turning large open-source datasets into interpretable research findings.
Overview
Faculty-guided research using Python, Excel, statistical testing, regression, visualization, and presentation to move from raw public data to analysis-ready evidence and clear conclusions.
Challenge
Open-source education and sports data needed to be collected, standardized, and analyzed before it could support meaningful research questions.
Approach
Built a repeatable data-cleaning and analysis workflow, applied statistical and regression methods, and translated results into visuals and presentation-ready findings.
Outcome
Collected, cleaned, and standardized 80,000+ open-source records and presented findings to non-technical audiences with practical implications for learning and athlete performance.