I analyze real-world data to surface patterns and insights, with externship experience in juvenile justice records.
Projects include a data analytics externship with the Center for Improving Youth Justice, working on multi-year juvenile justice datasets to identify meaningful patterns and outcomes.
Center for Improving Youth Justice · Sep 2026 · ✅ Verified by Extern
A data analytics externship with the Center for Improving Youth Justice focused on analyzing two decades of juvenile justice records to surface meaningful patterns in incidents, demographics, and outcomes.
Aggregated survey data (~5,000+ responses, 85 facilities) into facility-level metrics using Python (pandas). Ran and interpreted a regression analysis (statsmodels) linking staff and youth safety perceptions, then stress-tested it for bias and confounding. Directed Claude as a coding and analysis partner, independently verifying and revising its output throughout.
I analyze real-world data to surface patterns and insights, with externship experience in juvenile justice records.
I am a junior Data Science student at the University of Washington Bothell studying for a bachelor’s degree. I have some practical experience in data analytics, including an externship analyzing two decades of juvenile justice records to find patterns in incidents, demographics, and outcomes. I am focused on strengthening my resume and improving competitiveness for jobs and internships.
Externships
Data Analytics Externship with Center for Improving Youth Justice
Completed · Sep 2026
Experience
Residential Advisor
University of Washington Bothell · September 2025 – Present
Data Analyst Intern
Washington West African Center · July 2025 – August 2025
Peer Career Advisor
University of Washington Bothell · September 2023 – August 2025
Education
University of Washington Bothell
Bachelor of Science in Data Visualization · Class of 2027
Skills
✅ Verified by Extern
A data analytics externship with the Center for Improving Youth Justice focused on analyzing two decades of juvenile justice records to surface meaningful patterns in incidents, demographics, and outcomes.
The work began by inventorying the CIYJ workbook and consolidating staff and youth survey responses into facility-period means, then merging those files and documenting differing survey recall windows. A linear regression on the aggregated facility dataset (n=162 rows, 85 facilities) tested whether staff fear rate predicted youth fear rate, producing a slope of 0.341 (p<0.001) with R-squared
I produced a cataloged CIYJ workbook, a merged facility-period survey dataset covering 85 sites, a regression result showing staff fear rate predicted youth fear rate (slope 0.341, p<0.001; R-squared 0.124), and a brief with charts and slides summarizing the findings.
Aggregated survey data (~5,000+ responses, 85 facilities) into facility-level metrics using Python (pandas). Ran and interpreted a regression analysis (statsmodels) linking staff and youth safety perceptions, then stress-tested it for bias and confounding. Directed Claude as a coding and analysis partner, independently verifying and revising its output throughout.
Pulled U.S. Census ACS data via API in R, filtering 28,000+ variables to compile county-level cost-of-living datasets across Washington State and the U.S. Built an interactive geographic heatmap using tmap within a deployed Shiny web dashboard to visualize regional affordability trends. Tracked team contributions using Git/GitHub for version control, troubleshooting, and workflow coordination.
Wrangled job listing dataset with over 1.5 million entries using RStudio to prepare for visualization. Utilized R tidyverse packages such as ggplot2 to visualize trends in tech job listings over time in WA, TX, and CA. Created and presented project dashboard in a team of 5 to showcase graphs and findings while supporting them with peer-reviewed research papers. Documented data process in R Markdown HTML report summarizing findings and future tech job market trends.