Graduate Program — Data Science (M.S.)

UC San Diego

Sep 2024 - Present

  • End-to-end data science workflows: Built complete pipelines including data collection (CSV/JSON), cleaning, feature engineering, and analysis using Python, Pandas, NumPy, and SQL.
  • Exploratory data analysis and statistics: Performed exploratory analysis, statistical testing, and visualization to identify patterns and clearly communicate insights.
  • Machine learning modeling and optimization: Developed and optimized machine learning models with scikit-learn using cross-validation and hyperparameter tuning.
  • Reproducibility and transparency: Ensured analyses could be replicated through well-documented notebooks, version control, and structured pipelines.

Web Development (Self-Directed Practice)

Remote

Past

  • Data-Driven Applications: Developed and deployed small web applications while practicing modern frameworks (Next.js, HeroUI), version control (GitHub), and cloud deployment workflows (Vercel).