Data Cleaning & Preparation Quiz

Which of the following is NOT a common approach to handling missing data?

• Deleting rows with missing values

• Filling missing values with the mean or median

• Creating a separate category for missing values

• Randomly assigning values from another dataset

Which technique is best for detecting and handling outliers in numerical data?

• Standard deviation method

• Z-score normalization

• IQR (Interquartile Range) method

• All of the above

Which of the following is an example of duplicate data?

• Two customers with the same email but different phone numbers

• Two identical transaction records in an e-commerce dataset

• A missing value in the dataset

• A spelling mistake in a product name

In text data cleaning, what is the primary purpose of tokenization?

• Removing stopwords

• Converting text into individual words or phrases

• Correcting spelling errors

• Detecting outliers in numerical data

What is the purpose of standardizing data from multiple sources?

• To remove all numerical values from the dataset

• To ensure consistent formatting and representation across different datasets

• To make data more complex for analysis

• To delete unnecessary information

Imputing missing values with the mean is always the best method.

• True

• False

Data deduplication helps in improving the quality of a dataset by identifying and removing redundant entries.

• True

• False

Normalization and standardization are the same techniques used for data scaling.

• True

• False

Regular expressions (Regex) can be used for cleaning messy textual data by removing unwanted characters, symbols, and patterns.

• True

• False

Outliers should always be removed from a dataset.

• True

• False