A collection of projects exploring the intersection of mathematics, statistics, and computational methods to solve real-world problems through data-driven decision making.
This repository documents my journey in applied data science, showcasing hands-on implementations of analytical techniques ranging from exploratory data analysis and statistical modeling to optimization and machine learning. Each project reflects a commitment to rigorous methodology, clear communication, and practical problem-solving.
- Methodologically sound: Grounded in statistical theory and best practices
- Practically focused: Applied to meaningful problems with actionable insights
- Reproducible: Complete code, documentation, and analysis for transparency
- Evolving: Continuously updated with new projects and improvements
Python • R • SQL • Jupyter Notebooks • Git • LaTeX
See RUNNING.md for the tested Python environment, the R/Quarto execution commands, verification steps, and the small number of projects that require local datasets, a solver licence, or private API credentials.