Summary#

Strong foundation in the mathematical underpinnings of data science, with hands-on experience implementing core algorithms from scratch in R and Python. Published researcher who knows how to analyze data, validate findings, and communicate results through IEEE papers.

How I Apply This Skill#

• Implemented SVD, QR factorization, and eigenvalue analysis from scratch in R
• Applied PCA for dimensionality reduction on country-level socioeconomic data
• Built graph algorithms including Dijkstra’s shortest path and Markov chains
• Created data mining pipelines for healthcare analytics with association rules
• Produced publication-quality visualizations with Matplotlib and ggplot2
• Co-authored 3 IEEE papers on healthcare data analytics

Key Strengths#

• Linear Algebra: SVD, eigenvalues, matrix decomposition—the math behind ML
• Statistics: Regression, PCA, probability, hypothesis testing
• Graph Theory: Shortest path, network analysis, Markov chains
• Data Mining: Association rules, feature selection, classification
• Visualization: Matplotlib, ggplot2 for insights and publication

Related Projects#

• Long COVID Prediction - IEEE-published research
• Country Data Analysis - PCA application
• Singular Value Decomposition - Algorithm from scratch
• Gram-Schmidt & QR Factorization - Linear algebra
• Dijkstra’s Algorithm - Graph algorithms
• Markov Chains - Probability modeling