What is Data Wrangling?

• A. Cleaning and preparing unorganized and scattered data sets for easy access and analysis.
• B. Converts from one form to another.
• C. Storing data in data warehouses.
• D. Generating visualizations.

Answer: (A)

Data wrangling is the process of turning raw, messy data into a clean, usable dataset for analysis. It involves cleaning errors, handling missing values, standardizing formats, removing duplicates, and reconciling data from different sources so the data can be trusted and easily accessed by analytical tools. This preparation step is what makes deeper analysis possible and reliable, since you’re starting from data that’s consistent and ready to be explored.

Converting data from one form to another is a more specific task—data transformation—that can be a part of data wrangling but isn’t the whole activity. Storing data in data warehouses is about where data lives, not cleaning or preparing it. Generating visualizations is about presenting results, not the preparation work that enables accurate analysis.