Junior at Georgia State University studying Economics, preparing for a career in data analytics through coursework and hands-on analysis.
Portfolio pieces that show economic analysis, data cleaning, visualization, and quantitative reasoning developed while studying at Georgia State University.
Prepared and organized stock market and interest rate data for quantitative analysis through data cleaning. Conducted financial analysis using Python (Pandas) to evaluate market risk trends and model stock price movements.
Cleaned a 30,000-record dataset using Python to analyze the impact of social media usage on student productivity and mental health. Used R, ggplot2, and regression modeling to identify relationships between social media use, attention span, stress levels, and productivity outcomes. Presented research findings and data visualizations.
Junior at Georgia State University studying Economics, preparing for a career in data analytics through coursework and hands-on analysis.
I am Chinyelu Phil-ebosie, a junior Economics student at Georgia State University focused on a career in data analytics. I study economic theory and quantitative methods while building skills to analyze and communicate data-driven insights.
Experience
Data Analyst Intern
Griffin & Strong, P.C · September 2025 - May 2026
Student Research Assistant
Georgia Health Policy Center · March 2025 – May 2026
Advisory Intern
KPMG · June 2024 - July 2024
Education
Georgia State University
B.S., Economics, Minor: Computer Information Systems · Class of 2026
Skills
Prepared and organized stock market and interest rate data for quantitative analysis through data cleaning. Conducted financial analysis using Python (Pandas) to evaluate market risk trends and model stock price movements.
Cleaned a 30,000-record dataset using Python to analyze the impact of social media usage on student productivity and mental health. Used R, ggplot2, and regression modeling to identify relationships between social media use, attention span, stress levels, and productivity outcomes. Presented research findings and data visualizations.
Built multiple linear regression models in RStudio to assess how square footage and bedrooms affect housing prices. Evaluated model fit using R-squared and adjusted R-squared, and interpreted coefficients to explain real estate trends. Documented methodology and visualized results in Excel.
Built relational tables by joining patient, doctor, and admissions data to create a consolidated dataset for analysis. Applied SQL functions to handle missing data, calculate patient length of stay, and correct inconsistencies in date fields. Designed queries using string matching to identify and analyze diagnoses related to heart conditions.