Mohamed Khaled
Data Analyst | Clean and Visualize Data
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Building HYNOVA around a simple idea: helping small businesses replace messy, repetitive processes with better systems.
My work focuses on designing practical solutions across data, databases, reporting, workflow automation, and AI-assisted processes.
I’m developing systems that connect business data, automate repetitive workflows, improve reporting, and make day-to-day operations easier to manage.
Alongside the technical work, I handle the product thinking, documentation, positioning, and development of reusable systems that can eventually be adapted for different businesses.
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Analyzed a car-market dataset to understand pricing patterns, vehicle characteristics, and market segmentation. I cleaned and prepared the raw data, performed exploratory analysis using Python and Pandas, and developed a Power BI dashboard for interactive analysis.
The project examined relationships between vehicle price, mileage, age, manufacturer, fuel type, transmission, vehicle type, and other available attributes. I focused on turning the dataset into practical business questions and presenting the resulting patterns through clear, decision-oriented visualizations.
The project demonstrates an end-to-end workflow from raw data preparation through analysis, visualization, and business insight generation.
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13
Built a business-focused Excel reporting system from raw operational data.
The project involved cleaning and organizing the source data, creating reusable calculations, analyzing business performance, and developing an interactive dashboard for monitoring key metrics and trends.
I focused on making the workbook practical rather than simply visual—using structured tables, formulas, PivotTables, and repeatable reporting workflows so the analysis could be updated without rebuilding the entire report from scratch.
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16
Built an end-to-end football analytics project using 1.7M+ records covering matches, players, clubs, competitions, appearances, transfers, and valuations.
I cleaned and explored the data with Python, performed analytical work using SQL and SQLite, and developed an interactive Power BI dashboard covering club performance, league intelligence, match analysis, player performance, and the transfer market.
The project was designed around realistic questions a sporting or performance department might need to answer, with the focus on turning a large dataset into clear, decision-ready insights.
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