Experience


Data Science and Analytics Intern at Apigate, Malaysia

April, 2019 → Present

  • Worked with the Data Science and Analytics team to construct a data warehouse and analytics ecosystem on Google Cloud Platform (GCP) and do data migration. Had meetings with Google team to discuss GCP products and usage.
  • Developed an ETL data pipeline in Python to load production data from Salesforce into Google BigQuery warehouse with a suitable schema using Salesforce API and BigQuery API.
  • Analyzed Apigate data using Python and Google Data Studio to uncover useful business insights and patterns from the big amounts of data available.

Education


Master of Data Science at University of Malaya, Malaysia

2018 → (Sep 2019, expected)

  • 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.

B.Sc in Computer Engineering at Princess Sumaya University for Technology, Jordan

2012 → 2016

  • Got the first rank in the Computer Engineering department with a GPA of 93.9%
  • The program is acredited by ABET.
  • Received an 'Outstanding Student Award' for academic achievement in 2015 and other awards.
  • Relevant courses: Data Structures and Introduction to Algorrithms, Database Systems, Computer Architecture and Organization, Visual Programming, Discrete Mathematics, Calculus 3, Operating Systems, Signals and Systems, etc.

Skills


Programming Languages: Python, JavaScript, R.

Data analysis and machine learning: Pandas, Numpy, Scikit-learn, Jupyter Notebook, SQL, Hadoop ecosystem.

Cloud computing: Google Cloud Platform, Amazon Web Services (EC2, S3).

Text analysis and NLP: Regular expressions, Wordcloud, limited experience in NLTK.

Web scraping: BeautifulSoup, Selenium.

Data visualization: Matplotlib, Google Data Studio, Seaborn, limited experience in Tableau.

Web development: Frontend (HTML, CSS, JavaScript, Vue.js), Backend (Django), hosting and deployment.

Misc: Lyx (LaTeX IDE), Git, graphic design (Adobe Photoshop, GIMP), Google Analytics.

Languages: fluent in English (TOEFL: 102/120 in 2016), native Arabic, basic Malay.

Projects


YouTube Trending Video Analysis: Analyzed 40,000+ trending YouTube videos to answer interesting questions like “What are the most common words in video titles?”, “Which channels and which categories have the largest number of trending videos?”, and many more. (Python, Pandas, Matplotlib, Seaborn, WordCloud, Jupyter Notebook).

Analysis of Top Reddit posts: Analyzed the top 1,000 posts of 18 popular subreddits on reddit.com. Found the most common words and n-grams, used word clouds to show them, etc. (Python, Pandas, NLTK, WordCloud, Matplotlib, Seaborn).

Kaggle Machine-Learning Competitions: Participated in many competitions where a variety of ML techniques were used:

  • Help Navigate Robots: (best score: 1.0, worst: 0.0, mine: 0.6802 [top 5%], 1478 competitors, metric: multiclass-accuracy): [time-series data; multi-class classification; time-series feature extraction]
  • PUBG Finish Placement Prediction (best score: 0.01385, worst: 0.52662, mine: 0.04227, 1780 competitors, metric: MAE): [regression; 6+ million rows of data with limited CPU and RAM; extensive feature engineering]
  • VSB Power Line Fault Detection: (best score: 0.71899, worst: -0.28109, mine: 0.58655, 1595 competitors, metric: Matthews correlation coefficient): [classification; 10+ GB of data with limited CPU and RAM; signal processing; used stacking for modeling]

House Price Prediction, An End-to-End Machine-Learning Project: (Pandas, Matplotlib, Scikit-learn, XGBoost, etc.).

s3upload: Automates uploading files to AWS S3. (Python, boto3).

Focus Phase: An open-source time-tracker command-line Python application with statistics and visualizations. Published on the Python Package Index.

My personal website: Contains my portfolio, resume, and blog. Deployed on AWS. (frontend and backend development, Python, Django, JavaScript, HTML, CSS).

Pair & Compare: Makes it easier for developers to choose fonts and font-pairs for their projects. It allows trying out all Google font without downloading or installing any of them. (Vue.js, JavaScript, HTML, CSS, Web Font Loader).