Summary#

Data science and statistical computing experience in R, with particular strength in implementing mathematical algorithms from scratch. Built custom implementations of core linear algebra and statistical methods, verified against R’s built-in functions, demonstrating deep understanding of the mathematics behind data science.

How I Apply This Skill#

• Implemented SVD, QR factorization, and Gram-Schmidt algorithms from scratch
• Built eigenvalue analysis and pseudo-inverse computation
• Applied PCA for dimensionality reduction on real-world datasets
• Created graph algorithms including Dijkstra’s shortest path and Markov chains
• Developed regression methods including least squares and genetic algorithm optimization
• Produced publication-quality visualizations with ggplot2

Key Strengths#

• Linear Algebra: SVD, QR factorization, eigenvalue decomposition, matrix operations
• Statistics: Regression analysis, PCA, probability distributions
• Graph Algorithms: Shortest path, Markov chains, network analysis
• Visualization: ggplot2 for publication-quality graphics
• Mathematical Rigor: Implementing algorithms from mathematical specification

Related Projects#

• Gram-Schmidt & QR Factorization - Linear algebra from scratch
• Singular Value Decomposition - SVD and pseudo-inverse
• Dijkstra’s Algorithm - Graph shortest path
• Markov Chains - Probabilistic state modeling
• Country Data Analysis - PCA application
• Least Squares Problem - Regression from scratch
• Line of Best Fit - Genetic algorithm optimization
• University Graph Analysis - Analysis of university faculty relations