Mohammad Awad
Big Data Support Engineer at Microsoft
Data Science at Princess Sumaya University for Technology
Jordan
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Big Data Support Engineer at Microsoft
Experienced professional with a MSc in Data Science and 5+ years of experience in multiple domains including working in Microsoft's Big Data Support Team. Provides data consultations to empower major organizations to utilize the hidden information in their data.
Experience
Big Data Support Engineer
August 2023 - Present
- Provide consultation and technical support to Microsoft Azure Databricks EMEA customers.
- Specialized in Machine Learning, Data Governance, Workspace and Connectivity for Azure Databricks.
- Azure cloud resource management including Azure Virtual Networks, Storage, Virtual Machines, Key Vault, and Entra ID.
Data Scientist RA
June 2023 - April 2024
- Carried out an end-to-end machine learning research project utilizing fMRI brain imaging for Autism Spectrum Disorder detection.
- Provided state-of-the-art results through careful feature selection, engineering and machine learning model fine-tuning.
Education
Data Science
Princess Sumaya University for Technology
Graduated in 2024
Certificates & Badges
No certificates or badges added
Projects
Mapping PV Solar Panels in Amman Utilizing Computer Vision on Satellite Imagery
•https://github.com/mohammad-awad-ds/Solar_Panel_DetectionAndMapping_SatelliteImages_YOLOv5/tree/mainData Scientist
This is an end-to-end real world computer vision project for the detection and mapping of solar panels in the city of Amman.
Tools:
Tools used in this project:Google Colab, python, pandas, NumPy, labelme, scikit-learn, YOLOv5, Folium, urllib.request, shutil and OS.
Project Scope:
The project scope covers the following:Has a pipeline for scrapping tiles (satellite images as raw data) based on coordinates (ex: city of Amman) using an API.
Preprocesses the tiles into suitable size and format while recording the latitude and longitude coordinates for each tile to be used in model training and solar panel location mapping.
Manually labelling the PV solar panels appearing in the tiles.
Trains a high precision YOLOv5 deep learning model for PV solar panel detection installed in comercial and residential environments.
The solar panels detected by the model are then visually located on a map using the Folium python library based on the previously recorded coordinates.
Statistical analysis is conducted for the solar panel distribution over east and west Amman.
Languages
Arabic
Native
English
Professional
Skills
Data Analysis
Docker
SQL
Machine Learning
Python
Data Science
Data Cleaning
R
Big Data Engineer
Google TensorFlow
Data Visualization
MongoDB
NumPy
matplotlib
Pandas
Feature Engineering
Scikit-Learn
PostgreSQL
Microsoft Azure
Spark