University of California, Los Angeles
M.S., Applied Statistics and Data Science
Expected Dec 2026
Education
UCLA coursework built around statistics, data science, and applied modeling.
University of California, Los Angeles
B.S., Statistics and Data Science
Completed Jul 2024
Coursework and Learning Areas
These sections are set up so each course area can grow over time with more class-specific notes, tools, projects, and takeaways.
Statistical Consulting and Survey Analysis
Learned how to translate client questions into statistical workflows, clean survey data, compare pre/post responses, communicate limitations, and turn analysis into recommendations for a non-technical audience.
Machine Learning and Predictive Modeling
Built intuition for model selection, validation, feature preparation, classification metrics, Random Forests, Logistic Regression, SVMs, and how to explain model tradeoffs responsibly.
Statistical Computing with Python and R
Practiced cleaning, joining, reshaping, visualizing, and documenting datasets with pandas, NumPy, tidyverse, ggplot2, and reproducible notebook-style workflows.
Text Mining and Sentiment Analysis
Applied scraping, tokenization, word-frequency analysis, sentiment scoring, and narrative comparison to extract insight from unstructured text.
Data Visualization and Business Intelligence
Focused on dashboard design, visual clarity, stakeholder reporting, and choosing charts that make patterns, trends, and decisions easier to see.
Geospatial and Remote Sensing Analytics
Worked with multispectral imagery, raster data pipelines, environmental variables, and visual analysis for risk assessment and public-health research contexts.