Data Scientist | Problem Solver | Badminton Enthusiast
Data-driven by day, shuttle-driven by passion!
Data-driven problem solver with a robust foundation in data science and computer science, actively seeking opportunities to leverage analytical skills and technical expertise. My academic journey towards a B.S. in Data Science at San Jose State University has equipped me with hands-on proficiency in Python, C++, and Java, complemented by practical experience in managing large datasets and developing effective algorithms. Beyond the technical realm, my dedication extends to the badminton court, where strategic thinking, precise execution, and a collaborative spirit are paramount – qualities I bring to every analytical challenge. I am eager to contribute to innovative projects, translating complex data into actionable insights within a collaborative team environment.
This project explores trends in badminton racket and string preferences by analyzing player-submitted gear data. I conducted a detailed examination of string tension distributions to identify common patterns and performance preferences across different skill levels. Outlier detection techniques were applied to highlight unusual configurations, such as excessively high or low tension setups, offering insights into unique player strategies or potential data entry anomalies. The project combines domain knowledge in badminton with data analysis techniques to deliver actionable insights for both players and coaches interested in gear optimization. The data also updates every time the .csv file is pushed onto the GitHub using a Github workflow file.
The project being presented is a system built using Java and SQL which will include hospital operations. The purpose of this project is to improve hospital efficiency, reduce paper waste, and allow for real-time access to patient and hospital data for authorized medical staff. It is relevant because hospitals, through the use of this system, will have more resources to provide faster, more accurate services while improving the overall patient experience.
I leveraged Python to process and analyze multi-gigabyte datasets from various sports tournaments, playing a key role in developing a more accurate, data-driven player rating system. To enhance the analysis, I implemented a Minimum Spanning Tree (MST)-based adjacency matrix that visualized and quantified player overlap across tournaments. This enabled effective network analysis of player participation patterns, providing deeper insights into competitive dynamics and tournament structure.
Download my resume here.