Bachelor of Data Science
Umm Al-Qura University
About
I am a Data Science graduate from Umm Al-Qura University. I’m passionate about the full data lifecycle: collection, preprocessing, modeling, visualization, and communicating insights.
My interests include data analytics, business intelligence, machine learning, Power BI, SQL, and building data solutions that help teams make real decisions.
Umm Al-Qura University
Experience
Zimam Company · Seasonal
King Abdullah Medical City · COOP
Skills
Projects
Graduation project that classifies public stance toward women’s sports in Saudi Arabia using 1,348 annotated comments from X and YouTube. The project benchmarks AraBERT, ALLaM 7B, LLaMA 3.1 8B, and LoRA-ALLaM across supervised and few-shot settings.
Predictive analytics project designed to identify high-risk road areas during Hajj and Umrah seasons using synthetic data, Random Forest, Deep Learning, and interactive geospatial visualization with Folium.
Python data analysis project exploring bikeshare data from Chicago, New York City, and Washington. Users can filter by city, month, and weekday, then view travel time, station, trip duration, and user statistics.
IBM Data Science Capstone project predicting whether the Falcon 9 first stage will land successfully. It includes API data collection, web scraping, SQL analysis, EDA, dashboards, maps, and ML prediction.

Interactive Power BI dashboard analyzing global chocolate sales through KPI tracking, revenue trends, geographic insights, country-based filtering, and product performance analysis.
Interactive Tableau dashboard exploring global data science salaries across countries, experience levels, employment types, company sizes, and job roles through dynamic visualizations and business intelligence reporting.
SQL data exploration project analyzing a movie rental database to answer business questions about rental trends, movie categories, store performance, customer behavior, and monthly revenue patterns.
Computer vision project for classifying Fashion MNIST images into clothing categories using TensorFlow. The script includes data loading, preprocessing, model training, and evaluation workflow.
Supervised machine learning project using healthcare data to predict diabetes outcomes through classification models and evaluation metrics.
NLP project focused on text preprocessing, feature engineering, TF-IDF vectorization, sentiment classification, and model evaluation.
Certificates
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