Andrew Girgis

Economist | Data Scientist | Entrepreneur

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Welcome! I'm Andrew Girgis, a Data Scientist passionate about turning complex data into meaningful solutions.

Education

  • University of Waterloo
    • Master of Arts in Economics
    • Graduate Diploma in Computational Data Analytics
  • Brock University
    • BA Honours in Economics
    • Minor in Applied Computing

Technical Arsenal

  • Languages: Python, R, SQL
  • Data Science: pandas, NumPy, scikit-learn, TensorFlow
  • Focus Areas: Machine Learning, Econometrics, Data Visualization

Mission

Driving positive change through data-driven solutions. I'm committed to leveraging technology to solve real-world challenges and create sustainable impact.

Beyond Data

Enthusiast of automotive technology, physics, and emerging tech. Always eager to explore new frontiers and expand my knowledge horizon.

Explore My Latest Projects

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.