https://github.com/fiona1217/world-cup-database
A PostgreSQL database project that manages and analyzes World Cup game data. The application handles game and team information using a PostgreSQL database and includes various SQL queries for data analysis and reporting.
https://github.com/fiona1217/world-cup-database
database postgresql
Last synced: about 2 months ago
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A PostgreSQL database project that manages and analyzes World Cup game data. The application handles game and team information using a PostgreSQL database and includes various SQL queries for data analysis and reporting.
- Host: GitHub
- URL: https://github.com/fiona1217/world-cup-database
- Owner: Fiona1217
- Created: 2024-10-26T23:38:28.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-26T23:56:24.000Z (over 1 year ago)
- Last Synced: 2025-02-09T08:34:40.798Z (over 1 year ago)
- Topics: database, postgresql
- Language: Shell
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# World-Cup-Database Project ⚽
## Overview
This project involves the creation and management of a PostgreSQL database to store and analyze data related to FIFA World Cup games. The database consists of tables for teams and games, allowing for various queries to gain insights into tournament statistics.
## Key Learnings
### Database Design:
Understanding the importance of properly structuring tables to facilitate efficient data storage and retrieval. The design includes foreign keys to establish relationships between teams and their respective game results.
### SQL Queries:
Gaining proficiency in SQL through the execution of various queries to calculate statistics, such as total goals, average goals, and the number of games played. The ability to use aggregate functions like SUM, AVG, and COUNT has been developed.
### Data Manipulation:
Learning how to insert and update data within the database. This includes truncating tables for fresh data imports and ensuring the integrity of the data through checks and validations.
### Error Handling:
Encountering and resolving common SQL errors, such as syntax issues and logical errors in JOIN statements. This has strengthened problem-solving skills and attention to detail.
### Data Analysis:
Utilizing SQL to derive meaningful insights from the data, such as identifying winning teams and understanding scoring patterns across different tournaments.
### Bash Scripting:
Implementing shell scripts to automate database interactions, demonstrating how scripting can enhance efficiency in data management tasks.