
Developed a data-driven player statistics analysis feature for the iramler/slu_score_module_development repository, focusing on PWHL performance trends by age and position. Leveraged Python, R, and Jupyter notebooks to create scripts and visualizations that identify ideal player profiles for maximizing points per game. Addressed a data reliability issue by fixing a hardcoded file path in the R script, ensuring consistent loading of the PWHL_Final.csv dataset across environments. Demonstrated skills in data analysis, data visualization, and version control, delivering reproducible workflows that support performance optimization and scouting decisions. The work reflects a methodical approach to both feature development and maintenance.
March 2025 performance summary for iramler/slu_score_module_development: Delivered data-driven PWHL statistics analysis feature and fixed data loading path, enabling reliable, reproducible analyses to inform performance optimization and scouting decisions.
March 2025 performance summary for iramler/slu_score_module_development: Delivered data-driven PWHL statistics analysis feature and fixed data loading path, enabling reliable, reproducible analyses to inform performance optimization and scouting decisions.

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