Project Description: Data Cleaning in SQL on World Layoffs Dataset

This project focuses on performing essential data cleaning operations using SQL to prepare a dataset for analysis. Data cleaning is a critical step in data management, ensuring the accuracy and reliability of the dataset by addressing common issues like duplicates, inconsistent formats, and unnecessary columns.

Key Objectives:

1. Removing Duplicates:
   Identified and removed duplicate records based on primary and composite keys to maintain data integrity and eliminate redundancy.

2. Standardizing Dates:
   Transformed and standardized date formats across the dataset to ensure consistency, making it easier for further analysis and reporting.

3. Handling Null or Blank Values:

   • Replaced null values with meaningful default values or aggregated statistics (e.g., averages or medians) where applicable.
   • Removed records or flagged entries with excessive missing data for further inspection.

4. Dropping Unnecessary Columns:
   Identified and removed irrelevant or redundant columns that do not contribute to the analysis or insights, improving database performance and clarity.

Technologies Used:

• SQL: MySQL/PostgreSQL/SQL Server for writing efficient queries to clean and transform the data.
• Database Management Tools: Tools like MySQL Workbench, pgAdmin, or SQL Server Management Studio for data exploration and query execution.

Project Highlights:

• Applied advanced SQL techniques such as DISTINCT, GROUP BY, CASE, COALESCE, and ALTER TABLE to clean the dataset.
• Ensured data quality by validating changes through sample queries and pre/post-cleaning comparisons.
• Documented the entire cleaning process for transparency and reproducibility.

This project demonstrates expertise in handling messy data, a critical skill in data analysis and database management roles. The cleaned dataset is now ready for further exploration and visualization, enabling actionable insights and informed decision-making.