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SQL Project Repository: Database Management and Analysis

This repository contains two distinct projects: "EmployeeManagementDB" and "EPLMatchesAnalysis." Each project focuses on different aspects of database management and analysis in SQL.

EmployeeManagementDB

Project Overview

The "EmployeeManagementDB" project involves the creation and management of a comprehensive employee database. It includes the creation of two key tables, EmployeeDemographics and EmployeeSalary, along with various SQL queries to showcase different concepts such as SELECT, UPDATE, DELETE, PARTITION BY, CTE, Temp Tables, String Functions, Stored Procedures, and Subqueries.

SQL Script Contents

The SQL script in this project covers the following:

  • Table creation and data insertion for employee demographics and salary information.
  • Usage of CASE statements for conditional logic in SQL.
  • Introduction to HAVING clause for filtering results based on aggregate functions.
  • Implementation of temporary tables and common table expressions (CTE).
  • String functions usage, including TRIM, LTRIM, RTRIM, REPLACE, SUBSTRING, UPPER, and LOWER.
  • Creation and execution of stored procedures for efficient data retrieval.
  • Subqueries demonstrated in SELECT, FROM, and WHERE statements.

Project Name: EmployeeManagementDB

This project aims to provide a solid foundation for employee data management, salary analysis, and insightful queries for decision-making.

EPLMatchesAnalysis

Project Overview

The "EPLMatchesAnalysis" project focuses on data cleaning and analysis of English Premier League (EPL) statistics. It includes transforming and optimizing the eplmatches_stats table by addressing issues with the date and time columns. The project also involves date and time extraction, updating the table with new columns, and performing a detailed analysis of match data.

SQL Script Contents

The SQL script in this project covers the following:

  • Data cleaning steps for two EPL-related tables (epl_league_stats and eplmatches_stats).
  • Extraction and conversion of date and time information from the date_GMT column.
  • Updating the table with new columns to store transformed date and time values.
  • Detailed analysis of match data, including top matches by attendance and specific match conditions.

Project Name: EPLMatchesAnalysis

This project aims to enhance the quality of EPL match data and derive meaningful insights for further exploration and visualization.

Feel free to explore each project individually for a detailed understanding of their objectives, SQL script contents, and outcomes.

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