
Contributed to the UMD-INST627-Fall2024 repository by developing an initial NBA game data analysis notebook that explored team performance, playoff consistency, and the influence of three-point shooting and turnovers. Leveraged Python, pandas, and SQL within Jupyter Notebooks to structure and analyze data, while addressing technical challenges related to database connectivity and notebook execution. Diagnosed and documented a data access issue involving SQLite, then removed a non-functional notebook to restore project stability. Established a workflow for reproducible data analysis and set a foundation for future enhancements, demonstrating a methodical approach to both feature development and bug resolution in a data-driven environment.
November 2024 monthly summary focusing on features and bug fixes in the UMD-INST627-Fall2024 repository, delivering an initial NBA game data analysis notebook, addressing data access issues, stabilizing the project by removing a broken notebook, and setting the stage for robust data-driven insights through pandas/sqlite3 in Jupyter notebooks.
November 2024 monthly summary focusing on features and bug fixes in the UMD-INST627-Fall2024 repository, delivering an initial NBA game data analysis notebook, addressing data access issues, stabilizing the project by removing a broken notebook, and setting the stage for robust data-driven insights through pandas/sqlite3 in Jupyter notebooks.

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