Which process involves removing or replacing missing values, tackling outliers, and eliminating errors or inconsistencies?

Which process involves removing or replacing missing values, tackling outliers, and eliminating errors or inconsistencies?

• A. Data cleansing and transformation
• B. Data normalization
• C. Data labeling
• D. Data augmentation

Answer: (A)

Improving data quality by removing inaccuracies and inconsistencies is the main idea. The steps described—removing or replacing missing values, addressing outliers, and fixing errors or inconsistencies—are classic data cleaning activities. Cleaning ensures the data set is accurate and reliable for analysis, and transformation often accompanies cleaning to prepare data for use (such as formatting, encoding, or scaling). The other options describe different goals: normalization resizes data ranges, labeling assigns categories for supervised learning, and augmentation creates more data. Therefore, data cleansing and transformation best fits the described process.

Improving data quality by removing inaccuracies and inconsistencies is the main idea. The steps described—removing or replacing missing values, addressing outliers, and fixing errors or inconsistencies—are classic data cleaning activities. Cleaning ensures the data set is accurate and reliable for analysis, and transformation often accompanies cleaning to prepare data for use (such as formatting, encoding, or scaling). The other options describe different goals: normalization resizes data ranges, labeling assigns categories for supervised learning, and augmentation creates more data. Therefore, data cleansing and transformation best fits the described process.