I began my career as a teaching assistant in communications engineering, where I taught courses such as statistics, stochastic processes, and signal analysis. This academic experience sharpened my analytical thinking and laid the foundation for my transition into data analytics—a field where I discovered my passion for turning raw data into meaningful insights.
Over the past few years, I’ve built a strong skill set in Python libraries, Excel, and Power BI. I’ve applied these tools in projects ranging from data cleaning and preprocessing to dashboard creation and reporting, and even machine learning experiments. Each project reinforced my drive to connect theory with practice and deliver solutions that add real value.
I work as a freelance junior data scientist, where I can help businesses clean and structure their data, extract insights for decision-making, and experiment with machine learning models. What sets me apart is my mix of academic depth and hands-on project experience, along with a genuine eagerness to learn and grow through real-world challenges.
06/2024 – Present | Freelancer
05/2023 – Present | Head of Educational KPIs Unit @ AASTMT
06/2012 – 04/2023 | Data Analyst @ AASTMT
09/2001 – 05/2012 | Teaching Assistant @ AASTMT
2008 – 2012 | M.Sc. in Electronics & Communications Engineering @ AASTMT Cairo – Egypt
1994 – 1999 | B.Sc. in Electronics & Communications Engineering @ AASTMT Cairo – Egypt
06/2025 – Present | Digital Egypt Pioneers Initiative (DEPI) @ MCIT
02/2025 – 05/2025 | ITIDA+GIGs @ itida & E-Youth
03/2024 – 08/2024 | AI Programming with Python @ Bertelsmann & Udacity
01/2024 – 04/2024 | McKinsey Forward Program @ McKinsey & Co.
Extract temperature data for Cairo, Egypt from databases and compare it to global temperature averages. SQL was used to extract city-level and global temperature data for analysis in Microsoft Excel. Moving averages revealed that Cairo has been hotter than global averages and both temperatures have increased in recent decades. Charts were created comparing 5-year moving averages for Cairo and global temperatures.
Dog Breed classifier project which the model checks if an uploaded image is that of "a dog" or "not a dog". Also, if the uploaded image is that of a dog, the algorithm tells the user what dog breed it is. The Deep Learning model distinguishes between the 223 classes of dogs with an accuracy of over 82.89%.
In this project, a deep learning model was trained as an image classifier to recognize different species of flowers. The model was developed to classify 102 flower categories with an accuracy of 88.2%.
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