What is data cleaning? Which tools would you use for data cleaning? Why is it necessary?

Question

Accepted Answer

Data cleaning is the process of identifying and correcting errors or inconsistencies in data to improve its quality and reliability.

Data cleaning involves removing duplicate records, correcting inaccuracies, handling missing values, and standardizing data formats.
Tools commonly used for data cleaning include OpenRefine, Trifacta, and Talend.
Data cleaning is necessary to ensure accurate analysis and decision-making, prevent errors in reporting, and improve overall data quality.
For example, in a dataset containing customer information, data cleaning would involve removing duplicate entries, correcting misspelled names, and filling in missing addresses.

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