MAHMOUD
MOHAMED
Data Analyst
Turning data into insights with Python, SQL, and BI tools
Book Appointment
Turning data into insights with Python, SQL, and BI tools
Book AppointmentI’m a data analyst driven by curiosity, precision, and impact. I don’t just look at numbers — I uncover stories that drive smarter business decisions. With hands-on experience at Orange Business and collaborations with international teams like BT and SingTel, I’ve helped transform complex data into insights that deliver measurable results.
My work has saved organizations significant time and cost through automation, process optimization, and data quality initiatives — earning me recognition as a “Change Maker” for delivering outstanding business value and customer satisfaction.
I’m certified by IBM, Johns Hopkins University, and UC Davis in data analytics, visualization, and business intelligence. I combine technical proficiency in Python, SQL, Excel, and Power BI with strong business acumen — bridging the gap between data and decisions.
I believe that great analysis goes beyond reports — it fuels innovation, efficiency, and growth. I bring reliability, curiosity, and results-driven energy to every project I take on. If you’re looking for someone who not only understands data but knows how to make it matter — you’re in the right place.
Description:
This project explores Amazon’s product sales data to uncover key insights about sales trends, profit margins, and order behavior across the United States. By analyzing 3,200+ transaction records.
This project analyzes over 3,200 Amazon sales records across the U.S. to uncover insights on sales trends, profit margins, and order behavior.
It identifies top-performing categories and regions, explores the link between sales quantity and profitability, and offers data-driven recommendations for business growth.
Used Technologies:
Python
Pandas
NumPy
Matplotlib
Seaborn
Plotly Dashboards
Google Colab
Objectives:
1- Extract book data (title, price, rating, genre, availability) using web scraping techniques.
2- Clean, organize, and structure the scraped data into a usable format.
3- Explore pricing trends and relationships between genres, ratings, and book prices.
4- Visualize insights through clear, professional data visualizations.
Description:
This project explores data scraped from the website Books to Scrape, an online catalog designed for web scraping practice. The goal is to collect book-related information such as titles, prices, genres, ratings, and availability, and perform a data analysis to uncover insights about pricing patterns and relationships among features.
Used Technologies:
Python
Google Colab
Pandas
Requests
Matplotlib
Beautifulsoup
Web scraping
Tableau
Objectives:
1- Analyze minimum, maximum, and average temperatures over a 10-year period.
2- Compare temperature distributions across multiple cities.
3- Identify patterns and trends that may reflect climatic or regional differences.
4- Present findings using visual storytelling through charts and plots.
Description:
This project explores and compares the temperature patterns of five major cities — London, New York, Paris, Berlin, and Rome — from 2015 to 2025 using historical data from the Open-Meteo API.
Through exploratory data analysis (EDA) and visualization, the project uncovers key insights into seasonal variations, long-term temperature trends, and inter-city differences.
Used Technologies:
Python
Google Colab
Pandas
Matplotlib
Seaborn
Requests
Power BI
Objectives:
1- Analyze product profitability, order volume, and revenue trends.
2- Measure customer retention, repeat purchase behavior, and cohort patterns.
3- Detect top-performing products and categories.
4- Identify return rates and their impact on profit.
Description:
This project simulates an end-to-end e-commerce database containing customers, products, orders, order items, and returns.
The goal is to uncover insights about sales performance, customer behavior, and return patterns using advanced SQL queries.
Used Technologies:
Python
SQLite
Pandas
Google Colab
Objectives:
1- Identify key factors affecting employee retention.
2- Analyze salary progression and promotion frequency.
3- Compute department-level turnover rates.
4- Provide data-driven insights for HR policy decisions.
Description:
This project models a company’s HR database with employees, departments, salaries, promotions, and exits.
It aims to analyze workforce composition, salary growth, and retention patterns using advanced SQL.
Used Technologies:
Python
SQLite
Pandas
Google Colab
Objectives:
1- Evaluate delivery time efficiency by city.
2- Rank top restaurants by customer ratings and order volume.
3- Assess courier performance based on delivery speed and ratings.
4- Examine monthly revenue trends and customer retention.
Description:
This project analyzes the performance of a food-delivery platform that connects customers, restaurants, and couriers.
It focuses on delivery efficiency, customer satisfaction, and revenue growth using SQL analytics.
Used Technologies:
Python
SQLite
Pandas
Google Colab
E-Mail Address
m.m.abdelmeged@gmail.com
LinkedIn
in/engmahmoudmohamed
Phone Number
+20 109 749 0886
PDF Resume on Google Drive
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