European Soccer Database - Data Analysis using SQL

Motivation

I embarked on this project to delve into the world of data analysis using SQL and showcase my skills in exploring and extracting valuable insights from complex datasets. The European Soccer Database, with its rich and comprehensive collection of soccer-related data, provided the perfect opportunity to apply SQL techniques and gain practical experience in data manipulation, aggregation, and analysis.

Important Definitions in SQL

Before diving into the analysis, it's crucial to understand some key concepts in SQL that will be utilized throughout the project:

Connection to the Database: Establishing a connection to the database and exploring available tables.
List of Countries: Extracting information about the countries represented in the dataset.
List of Leagues and Their Countries: Retrieving details about various soccer leagues and their corresponding countries.
List of Teams: Exploring the teams participating in the leagues.
List of Matches: Gathering insights from the match data.
Basic Analytics: Performing foundational analytics to gain initial insights.

Project Overview

The European Soccer Database is a comprehensive dataset tailored for soccer enthusiasts, analysts, and machine learning practitioners. With its vast collection of over 25,000 matches, 10,000 players, and detailed attributes sourced from the FIFA video game series, this dataset offers a treasure trove of information for analysis and exploration.

What's Included

Matches: More than 25,000 recorded matches from 2008 to 2016 across 11 European countries.
Players: Data on over 10,000 players, including attributes from the FIFA video game series.
Leagues: Information about the lead championship leagues in 11 European countries.
Team Lineups: Detailed squad formation with X, Y coordinates.
Betting Odds: Betting odds data from up to 10 providers.
Match Events: In-depth match events such as goal types, possession, corners, fouls, and more.

Project Structure

Connection and Exploration: Establishing a connection to the database and exploring available tables.
List of Countries: Retrieving and analyzing information about the countries in the dataset.
Leagues and Teams: Exploring leagues, their corresponding countries, and teams participating.
Matches and Analytics: Analyzing match data to extract insights and perform basic analytics.
Query Run Order: Understanding the sequence in which SQL queries are executed.
Subqueries and Functions: Utilizing subqueries and functions for advanced data manipulation.

Getting Started

To replicate or build upon this analysis, follow these steps:

Dataset: Download the European Soccer Database and set up a local database.
Clone: Clone this repository to your local machine using git clone https://github.com/Gokul-Raja84/Data-Analysis-using-SQL.
Run: Execute the SQL scripts in the provided Jupyter Notebook or your preferred SQL environment.

Contributions and Feedback

Contributions, suggestions, and feedback are welcomed and appreciated. If you find any issues, have improvements to suggest, or want to collaborate, feel free to open an issue or submit a pull request.

Acknowledgments

I would like to express my gratitude to the creators and maintainers of the European Soccer Database for providing this remarkable dataset for analysis. This project wouldn't have been possible without their efforts.

Feel free to delve into the database schema, experiment with different query optimizations, and contribute to enhancing the database's efficiency and performance.

Gokul-Raja84/Data-Analysis-using-SQL

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