What is the difference between data validation and data cleaning?

What is the difference between data validation and data cleaning?

Data validation ensures that data is accurate and meets certain criteria, while data cleaning involves correcting or removing inaccurate data.

Can you give me examples of data validation techniques?

Sure! Examples include range checks, format checks, and consistency checks to ensure data integrity.

What are some common data cleaning methods?

Common methods include removing duplicates, filling in missing values, and correcting typos.

Why is data validation important?

Data validation is crucial because it helps maintain data quality, which is essential for accurate analysis and decision-making.

How do data validation and cleaning work together?

They work together by first validating data to ensure it's correct, then cleaning it to enhance its quality for analysis.