Sophomore Data Science major who applies data analysis to e-commerce problems, practiced through a Breaking Games externship.
Portfolio of externship and coursework work that uses data analysis to inform e-commerce and product decisions, with emphasis on practical methods and clear recommendations.
Analysed GDP per capita and Human Capital Index data across 100 countries, applying ANOVA, correlation and linear regression to identify regional differences and economic patterns. Interpreted model outputs and residual diagnostics, translating quantitative analysis into clear economic conclusions
Cleaned and consolidated multiple real-world datasets and used SQL to analyse regional trends, population needs and disparities across provinces. Built Power BI dashboards and translated findings into clear visuals and recommendations for non-technical stakeholders.
Sophomore Data Science major who applies data analysis to e-commerce problems, practiced through a Breaking Games externship.
I am Chiamaka Udoye, a second-year Data Science student working toward a bachelor’s degree. I have limited professional experience and I completed the Breaking Games E-Commerce Data Analysis & Strategy externship to practice applying data analysis to product and commerce questions.
Experience
Facility Assistant
NHS, Highbury Hospital (Nottingham) · 09/2025 – Present
Activity Leader
SI5 Spy Mission Ltd, Nottingham · 10/2023 – 09/2025
SMBC Women in Banking Insight Day
SMBC Group EMEA, London · 08/2026
HSBC Women in Banking Insight Programme
HSBC · 09/2025
Education
Nottingham Trent University
BSc Mathematics with data science (Predicted: First class)
Pendleton sixth form college
BTEC Applied Science (Biology, Chemistry, Physics) - grades: D*D*D
Skills
Analysed GDP per capita and Human Capital Index data across 100 countries, applying ANOVA, correlation and linear regression to identify regional differences and economic patterns. Interpreted model outputs and residual diagnostics, translating quantitative analysis into clear economic conclusions
Cleaned and consolidated multiple real-world datasets and used SQL to analyse regional trends, population needs and disparities across provinces. Built Power BI dashboards and translated findings into clear visuals and recommendations for non-technical stakeholders.
Analysed 7,043 customer records across 20 features to identify key drivers of customer churn and assess their business implications. Prepared data for modelling, trained a decision-tree classifier and translated feature-level findings into practical recommendations.
Analysed a large-scale Kaggle survey dataset, engineering features and using clustering and classification models to identify structural drivers of income. Compared models using accuracy, precision, recall and F1, then communicated insights linking job role, industry, company size and work activities to earning potential.