Data-Cleaning-Project

This project cleans and prepares a layoffs dataset using SQL by removing duplicates, standardizing text, handling null/blank values, and dropping unnecessary columns. The result is a consistent, high-quality dataset ready for reporting, analysis, and visualization.
README Data Cleaning.md

🗃️ Data Cleaning Project (SQL)

📌 Project Overview

This project demonstrates data cleaning techniques using SQL on a layoffs dataset.
The goal is to transform raw, messy data into a clean and usable form for reporting and analysis.

Key objectives of this project:

• Remove duplicate records.
• Standardize text data (e.g., fixing inconsistent company/industry names).
• Handle null and blank values.
• Convert data types (especially dates).
• Drop unnecessary columns.

By the end of this process, we have a cleaned table (layoffs_staging2) ready for further analysis and reporting.

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🗂️ Files in this Repository

• Data Cleaning project.sql → The SQL script containing all steps of the data cleaning process.

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⚙️ Tools & Technologies

• Microsoft SQL Server (T-SQL)
• Database: layoffs

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🔑 Steps in Data Cleaning

1. Remove Duplicates

• Used CTE (Common Table Expression) with ROW_NUMBER() to identify duplicate rows based on company, industry, location, date, etc.
• Deleted rows where row_num > 1.

2. Standardize Data

• Trimmed extra spaces in company names.
• Corrected inconsistent industry names (e.g., "crypto currency" → "crypto").
• Fixed country names with trailing spaces or punctuation (e.g., "United States." → "United States").

3. Handle Null & Blank Values

• Removed rows with invalid or blank values (e.g., when industry contained dates instead of industry names).
• Ensured fields like date are in valid formats.

4. Convert Dates

• Used TRY_CONVERT(DATE, [date], 101) to change date strings into proper DATE format.

5. Drop Unnecessary Columns

• Removed helper columns like row_num after data cleaning was complete.

🎯 Key Learnings

• How to use CTEs and window functions for duplicate removal.
• Practical text standardization techniques with TRIM() and UPDATE.
• Best practices for handling missing or invalid data.
• Converting and validating date fields in SQL.
• Structuring a data cleaning workflow in SQL.