FIFA Player Database — SQL Analysis

An end-to-end SQL data analysis project designed to explore a FIFA player database and answer 12 key business questions. The project covers data extraction, filtering, multi-level aggregations, and advanced subqueries to extract actionable player and club insights. Developed as part of the National Telecommunication Institute (NTI) training program. PPTX Dataset Summary Volume: 19,667 player records across 164 countries and 1,009 clubs. Features: 9 core attributes including Name, Country, Position, Age, Overall_Rating, Future_Potential, Team, Value_Per_M$, and Total_Stats_Score. Key SQL Techniques & Query Breakdown Basic Exploration (Easy): Applied SELECT, WHERE, COUNT, and ORDER BY / TOP to preview records, filter by nationality (e.g., isolating 31 Egyptian players), compute overall age averages, and rank top-rated players. Data Aggregation (Medium): Leveraged GROUP BY and HAVING to determine player distributions per position, identify the most expensive talents, analyze average ratings by country, and segment clubs with large squads (> 20 players). Advanced Subqueries (Nested Queries): Implemented single and multi-nested subqueries to benchmark individual player ratings and team market valuations against calculated global dataset averages. Tools Used Database Management: SQL Server Management Studio