

Analytical Business Analytics student with hands-on experience in Python-based data analysis, machine learning, and statistical process improvement. Strong background in data cleaning, exploratory data analysis, and model evaluation, with a proven ability to translate complex datasets into clear, actionable insights. Demonstrates teamwork, communication, and discipline developed through Division I athletics and international experience.
Programming and data: Python (pandas, NumPy, scikit-learn), SQL, Excel, JMP
Data science: data cleaning and preprocessing, exploratory data analysis, feature engineering, model evaluation, classification and clustering, neural networks
Visualization: Matplotlib, Seaborn, Tableau, Power BI
Process and Quality: Six Sigma, statistical process control, Pareto, and root-cause analysis
Professional skills: analytical thinking, problem solving, team collaboration, clear communication
NBA Analytics & Machine Learning Project - Python
• Cleaned, preprocessed, and analyzed 10,000+ NBA player and team records
• Conducted exploratory data analysis (EDA) to identify performance patterns and trends
• Built and evaluated supervised and unsupervised machine learning models
(logistic regression, clustering) using scikit-learn
• Improved prediction accuracy by 22% through feature engineering and model tuning
• Communicated insights using data visualizations for technical and non-technical audiences
Healthcare Operations Database - SQL
• Designed and implemented a relational database (10+ tables) to manage patient and
appointment data
• Applied normalization and indexing to ensure data integrity and reduce query time by 30%
• Wrote SQL queries and ad-hoc reports to support operational and scheduling decisions