Professional Summary
Master of Science in Data Science (In Progress) at the University of Pittsburgh, with strong foundations in Python, SQL, machine learning, and statistical analysis. Experienced in nutrition and health-related data analysis, with hands-on experience in data cleaning, exploratory analysis, predictive modeling, and communicating insights to technical and non-technical audiences. Actively seeking Data Scientist or Machine Learning Intern roles in healthcare or product-focused teams.
Education
University of Pittsburgh
Master of Science in Data Science (In Progress)
- Core coursework: Machine Learning, Statistical Modeling, Data Management, Data Visualization
- Tools & methods: Python (pandas, NumPy, scikit-learn), SQL, Git, Jupyter, hypothesis testing
Technical Skills
Programming: Python, SQL, R (basic)
Data Analysis: pandas, NumPy, exploratory data analysis (EDA)
Machine Learning: regression, classification, clustering, model evaluation
Databases: MySQL, PostgreSQL
Visualization: Matplotlib, Seaborn, basic Tableau
Tools: Git/GitHub, Jupyter Notebook, Excel
Selected Projects
Nutrition & Health Data Analysis
- Cleaned and analyzed large-scale nutrition and health datasets to identify trends and risk factors.
- Applied regression and classification models to predict health-related outcomes.
- Communicated findings using clear visualizations and concise written summaries.
SQL & Business Analytics Case Studies
- Designed complex SQL queries (joins, window functions, CTEs) to answer business and product questions.
- Translated analytical results into actionable insights for decision-making.
Relevant Experience
Data Analysis & Research Projects (Academic)
- Performed end-to-end data workflows including data collection, cleaning, modeling, and reporting.
- Collaborated on team-based projects following reproducible research and version control best practices.
- Focused on interpretability and real-world impact of analytical results.
Interests
- Healthcare & public health analytics
- Applied machine learning for real-world problems
- Data-driven product decision making