Rawan Ahmed

• Studied linear algebra, calculus, and probability for data modeling and analysis.
• Learned to apply parametric (e.g., t-tests, regression) and non-parametric (e.g., chi-square, Wilcoxon) methods based on data assumptions.
• Gained experience selecting appropriate statistical tests for real-world datasets.
• Used R and SAS to perform hypothesis testing and interpret results.

Heart Disease Data Analysis – R & Clustering
Personal Project

• Analyzed a heart disease dataset using clustering techniques to identify patient risk groups.
• Used excel to clean data using IQR method and utilizes R to perform exploratory analysis, and visualize clusters.
• Derived actionable insights for each patient category, proposing targeted prevention strategies based on shared characteristics (e.g., cholesterol levels, age, lifestyle).
• Demonstrated ability to apply unsupervised learning and interpret results to support real-world health decision-making.

Machine Learning Project – WHO Life: A Global Initiative (Kaggle Competition) (In Progress)

• Building predictive models to estimate life expectancy across countries using socioeconomic and health-related indicators.
• Applying regression techniques (e.g., linear regression) to forecast life expectancy based on features like GDP, education, and health expenditure.
• Conducting data cleaning, feature selection, and model tuning using Python (pandas)
• Analyzing global disparities and drawing insights to support data-driven public health policies.

Does Justice Play Fair? An Econometric Analysis of Sentencing Disparities
Academic Project – Econometrics Course

• Investigated whether race, gender, and socioeconomic status significantly influence sentence length in U.S. federal court cases.
• Used multiple regression analysis with fixed effects to control for offense type, criminal history, district, and year.
• Conducted hypothesis testing:
  H₀: No sentencing disparities exist based on demographics
  H₁: Disparities persist even after controlling for legal factors
• Applied econometric techniques in R to test for bias and support fair sentencing policy recommendations.
• Excellent communication skills, both verbal and written.

• Critical thinking skills
• Understanding a line of reasoning
• Developed strong writing and editing skills for clear, concise communication.
• Practiced critical reading and analysis of complex texts and arguments.
• Gained experience structuring logical, persuasive reports—useful for data storytelling.
• Improved ability to explain technical ideas to non-technical audiences.

Event Announcer – School Poetry Competition
New Generation International Schools — 2018

• Delivered clear, engaging announcements to a live audience, ensuring smooth event flow.
• Practiced public speaking and time management under pressure.
• Represented the event with professionalism and confidence in a formal setting.
• Strengthened verbal communication skills useful for presentations and stakeholder engagement.