Skills

These are the tools and skills I’m developing through coursework, projects, and internships. I use them to prepare data, build models, and explain what I find.

Programming Languages

  • Python — Data analysis, visualization, and machine learning
  • Java — Learning and strengthening programming fundamentals
  • SQL — Basic querying and data retrieval

Data Analysis & Visualization

  • Pandas and NumPy for data cleaning and manipulation
  • Matplotlib and Seaborn for statistical visualization
  • Plotly for interactive visualizations
  • Exploratory data analysis and feature exploration

Machine Learning

  • Scikit-learn for regression and classification
  • Model evaluation using appropriate performance metrics
  • Feature engineering and data preprocessing
  • Understanding of Random Forest and Gradient Boosting

Data & Research Skills

  • Data storytelling and communicating insights
  • Statistical thinking and interpreting results
  • Working with real-world, messy datasets
  • Reproducible analysis and structured workflows

Tools & Platforms

  • Jupyter Notebook and VS Code
  • Git and GitHub for version control
  • Microsoft Excel for organizing and reviewing data