Master of Data Science | GPA: 3.98/4

2018 → 2019

University of Malaya, Malaysia

  • Research project: built and deployed a machine-learning model to predict taxi-trip duration. The process involved data preparation, data exploration, feature engineering, cross-validation, hyperparameter optimization, ensemble methods, etc. The resulting model was deployed using as a Flask application on Google App Engine. The project is available on
  • Relevant courses: Data Analytics, Programming for Data Science, Big Data Application and Analytics, Big Data Management, Data Mining, Principles of Data Science, Machine Learning for Data Science.

Bachelor of Science in Computer Engineering | GPA: 93.9% (1st rank)

2012 → 2016

Princess Sumaya University for Technology, Jordan

  • Some relevant courses: Data Structures and Introduction to Algorrithms, Database Systems, Visual Programming, Discrete Mathematics, etc.


Data Science and Analytics Intern at Apigate, Malaysia

April, 2019 → July, 2019

  • Worked on creating a data warehouse for the company on Google Cloud Platform (BigQuery) to store all company data. Toward that end, I created a Python ETL pipeline that runs weekly to move company data from Salesforce to the data warehouse.
  • Analyzed some of the company data (tens of millions of records) to get business insights and then present those insights to the management using proper format.


Python, machine learning (Scikit-learn, TensorFlow, XGBoost, LightGBM, CatBoost), data analysis (Pandas, Numpy, R), data visualization (Matplotlib, Seaborn, Google Data Studio), web scraping (BeautifulSoup), web development (JavaScript, Flask, Django), databases (SQL), cloud computing (Google Cloud Platform), Hadoop


Data analysis: analyzed interesting dataset to get useful insights and patterns. Some of them are:

Machine learning: Participated in many Kaggle competitions with regression and classification tasks. Some are “Help Navigate Robots” (ranked top 5%), “VSB Power Line Fault Detection”, and “PUBG Finish Placement Prediction”. The code used in these competitions can be found on

End-to-end data-science projects: In addition to the master-degree project mentioned above, a project was done to build a model for house-prices prediction; it can be found on

Other projects:

  • Pair & Compare: a web application that helps developers in choosing and comparing fonts. Available on
  • Focus Phase: an open-source Python application for time-tracking; it is published on Python Package Index. Available on
  • S3upload: an open-source Python application that makes it easier to upload files to AWS S3. Available on