I am a Computer Science student eager to harness data analytics for social good. Let's make a difference together!
Discover my projects in data analytics and technology, aimed at promoting social change and justice.
Center for Improving Youth Justice · Aug 2026 · ✅ Verified by Extern
A data analytics externship with the Center for Improving Youth Justice, focused on analyzing CIYJ's multi-year youth and facility datasets.
Researched and developed an original, data-driven analysis of regional housing affordability built entirely from public records, gathering and merging U.S. Census Bureau (API) and HUD datasets using Python and SQL. Cleaned and organized large, messy multi-source datasets, resolving inconsistent geographies, missing values, and definitional mismatches across sources to produce a reliable analysis-ready dataset. Analyzed cost burden, rent-to-income ratios, and demographic patterns to identify trends in housing accessibility, and built interactive Tableau dashboards to visualize the findings. Authored a structured, policy-style report translating the analysis into a clear narrative with data-backed recommendations for non-technical decision-makers.
Built an end-to-end pipeline collecting public economic data from the Federal Reserve (FRED API) into SQLite using Python, tracking indicators such as inflation, unemployment, and interest rates over time. Produced Matplotlib and Plotly visualizations that turn complex macroeconomic series into accessible visual narratives of economic trends. Documented data sources, definitions, and methodology to make the analysis transparent and reproducible (GitHub).
Designed and configured a relational database in SQL Server, creating tables, keys, constraints, indexes, and user access permissions; published full build as a 12-file GitHub repository. Monitored performance and analyzed query execution plans, improving SQL efficiency through indexing and query optimization.
I am a Computer Science student eager to harness data analytics for social good. Let's make a difference together!
I'm a graduate student at the University of Maryland Baltimore County, pursuing a Bachelor's Degree in Computer Science. Currently, I am gaining practical experience through a Data Analytics Externship at the Center for Improving Youth Justice. My goal is to leverage technology to create positive change.
Externships
Data Analytics Externship with Center for Improving Youth Justice
Completed · Aug 2026
Experience
Student IT Associate (Part-Time)
UMBC CHASS-IT · Mar 2023 – May 2026
Global Ambassador
UMBC Intl. Students & Scholars Office · Jul 2024 – May 2026
Data Analyst | SOJUNADAT (Volunteer)
SOJUNADAT · Jul 2024 – Mar 2026
Full-Stack Development Intern (Remote)
A Round Entertainment · May 2024 – Jul 2024
Financial Service Intern
Northwestern Mutual · Jun 2022 – Jul 2022
Education
UMBC, Catonsville, MD
B.S. Computer Science (Data Science track) & B.S. Financial Economics · Class of 2026
Quantitative Finance Bootcamp — Professional Development
Skills
✅ Verified by Extern
A data analytics externship with the Center for Improving Youth Justice, focused on analyzing CIYJ's multi-year youth and facility datasets.
The work examined CIYJ's primary dataset and related files, inventorying tables, mapping relationships, and running phase-one exploratory data analysis in Python. The primary sheet was recorded as 4,980 rows by 115 columns, each row representing a youth survey response, and a separate codebook sheet was located in the same file. The analysis aggregated Youth Climate and Incident files to
I documented the primary sheet as 4,980 rows by 115 columns with one youth survey response per row, located the codebook sheet, aggregated Youth Climate and Incident files to facility-period, joined them, and tested the association between facility-level medical-helpfulness scores and facility incident rates involving medical staff.
Researched and developed an original, data-driven analysis of regional housing affordability built entirely from public records, gathering and merging U.S. Census Bureau (API) and HUD datasets using Python and SQL. Cleaned and organized large, messy multi-source datasets, resolving inconsistent geographies, missing values, and definitional mismatches across sources to produce a reliable analysis-ready dataset. Analyzed cost burden, rent-to-income ratios, and demographic patterns to identify trends in housing accessibility, and built interactive Tableau dashboards to visualize the findings. Authored a structured, policy-style report translating the analysis into a clear narrative with data-backed recommendations for non-technical decision-makers.
Built an end-to-end pipeline collecting public economic data from the Federal Reserve (FRED API) into SQLite using Python, tracking indicators such as inflation, unemployment, and interest rates over time. Produced Matplotlib and Plotly visualizations that turn complex macroeconomic series into accessible visual narratives of economic trends. Documented data sources, definitions, and methodology to make the analysis transparent and reproducible (GitHub).
Designed and configured a relational database in SQL Server, creating tables, keys, constraints, indexes, and user access permissions; published full build as a 12-file GitHub repository. Monitored performance and analyzed query execution plans, improving SQL efficiency through indexing and query optimization.
