What is data cleaning in the context of the Large Data Set?

• A. Correcting or removing inaccurate, inconsistent, or duplicate records before analysis.
• B. Creating new data by combining existing datasets.
• C. Sorting data alphabetically by category.
• D. Encrypting data to protect privacy.

Answer: (A)

Cleaning data means fixing problems in the data so it’s reliable for analysis. It involves correcting errors in records, removing duplicates, and resolving inconsistencies or missing values before you analyze anything. In a Large Data Set, this step is crucial because even small mistakes or repeated entries can distort results or lead to misleading conclusions once you start looking for patterns or building models. By cleaning, you ensure that each observation is consistent and that comparisons across records are valid. This idea matches correcting or removing inaccurate, inconsistent, or duplicate records before analysis. The other options describe different tasks: creating new data by combining datasets is data merging, sorting data alphabetically is just organizing, and encrypting data is about protecting privacy.