Data Scientist
Mohamed Ayman is an aspiring Data Analyst and Data Science Enthusiast with strong foundations in data structures, algorithms, and problem-solving. He is skilled in Python, SQL, Excel, and Power BI for data analysis, visualization, and extracting actionable insights. Passionate about transforming data into meaningful insights, he continuously develops skills in data-driven decision making.
Leveraging Python, SQL, Excel, and Power BI to analyze data, create visualizations, and extract actionable insights for data-driven decision making.
Proficient in solving complex algorithmic and data structure problems, demonstrated through competitive programming achievements and mentoring students.
Experienced in developing applications using various programming languages and applying Object-Oriented Programming principles for robust and scalable solutions.
Developed intelligent systems utilizing NLP techniques, such as Bi-Gram language models, for real-time text prediction and auto-completion.
A Machine Learning project for heart disease prediction using clinical patient data. The project compares Logistic Regression, Random Forest, XGBoost, and Deep Learning (MLP), and combines them into a Weighted Soft Voting Hybrid Ensemble. The system also includes an interactive Gradio interface for real-time predictions.
Impact: Achieved 91.30% accuracy, 92.23% F1-score, and 93.81% ROC-AUC using a weighted soft-voting ensemble of Logistic Regressi
Developed an interactive healthcare analytics dashboard using Power BI to analyze patient demographics, hospital performance, and treatment costs.
Impact: Enabled data-driven insights into patient distribution and hospital efficiency by visualizing key metrics such as admissions, billing amounts, and length of stay.
Engineered an intelligent auto-completion system leveraging a Bi-Gram language model trained on CEFR-leveled datasets, featuring real-time word prediction through a Tkinter GUI.
Impact: Improved word prediction accuracy in a real-time typing interface by implementing a preprocessing pipeline including tokenization and frequency analysis.
Qualified for the ECPC (Egyptian Collegiate Programming Contest) and ranked 62/2000 participants.
Competed in the Meta Hacker Cup, a global programming competition.
Started mentoring students in advanced algorithms and competitive programming as an Instructor at Thebes CPC Community.
Developed a robust console-based banking system using Java OOP and exception handling.
Implemented a command-line library management system applying key OOP principles in C++.
Expected to graduate with a Bachelor of Computer Science, concentrating in Data Science.