Andrew Girgis's Portfolio

Economist & Data Scientist

About Me

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Hi, I'm Andrew Girgis – an aspiring data scientist with a strong foundation in econometrics, data analytics, and data visualization.

I'm currently pursuing a Master of Arts in Economics and a Graduate Diploma in Computational Data Analytics at the University of Waterloo, where I’m deepening my expertise in leveraging data to solve complex problems and present the data in a meaningful way. With a Bachelor of Arts Honours in Economics and a minor in Applied Computing from Brock University, I've built a robust analytical and technical skill set. My proficiency in Python, R, SQL, and key libraries like pandas, NumPy, and TensorFlow allows me to translate data into actionable insights and impactful solutions.

My career goal is to excel in data science, where I can contribute meaningfully to tech-forward projects and drive sustainable, positive change. I'm excited about using data to build a future where technology solves real-world challenges.

Outside of my professional pursuits, I am passionate about automotives, technology, and physics, and I actively seek opportunities to expand my knowledge and skills.

Feel free to reach out. Let's connect!

Projects & Tools

Canada with lightbulb's indicating innovation

Small businesses... Big impact

This study explores the impact of government funding on the innovation and growth of small businesses within a country's economy. It aims to analyze how financial support influences entrepreneurial success, technological advancements, and overall economic development.

COMING SOON!
Can we predict the future of the cars of the future: A study on predictors of BEV car manufacturer stock returns

Can we predict the future of the cars of the future: A study on predictors of BEV car manufacturer stock returns

This paper explores predictors of stock market returns for Battery Electric Vehicle (BEV) startups using GARCH and VAR models to forecast volatility and returns. Model accuracy is evaluated via mean absolute percent error. The study identifies optimal forecasting models, assesses volatility and return predictions, and finds the proposed predictors inadequate for accurate forecasting.

Gephi word network

Understanding Public Opinion Through Text Similarity and Sentiment Analysis

This project analyzes the relationship between an article and its comments using text similarity and sentiment analysis techniques. It applies Jaccard and Semantic Similarity scores to measure lexical and contextual overlap and uses sentiment intensity analysis to gauge the emotional tone of comments. Insights are visualized with Gephi to highlight key themes and connections within positive and negative sentiments.