SQL Data Cleaning and EDA Projects

This repository showcases SQL Data Cleaning and Exploratory Data Analysis (EDA) projects using MySQL.

Project 1: Data Cleaning

Overview:

The first project focuses on cleaning a dataset containing layoff data from around the world. It involves several steps to ensure data integrity and consistency.

Steps:

1. Removing Duplicates: Identifying and removing duplicate rows from the dataset.
2. Standardizing the Data: Identifying and fixing data issues like standardizing company names, industry names, and country names.
3. Handling Null or Blank Values: Populate missing values and handle null or blank entries in the dataset.
4. Removing Irrelevant Columns: Safeguarding the data by creating a new table mirroring the original one and deleting unnecessary rows.

Project 2: Exploratory Data Analysis (EDA)

Overview:

The second project involves conducting exploratory data analysis (EDA) on the cleaned dataset to gain insights and extract useful information.

Analysis:

1. Analyze layoffs by various factors such as industry, country, location, and stage.
2. Calculate total funds raised by each company per year.
3. Determine the date range of the dataset and analyze layoffs by date, year, and month.
4. Calculate rolling total of layoffs by month to identify trends over time.
5. Analyze layoffs by company and year, displaying the top 5 companies with the highest total layoffs each year.

Both projects are implemented using SQL queries executed using MySQL.