Data Cleaning Basics for Beginners

1. Which imputation method replaces missing values with the most frequent value?

Mean imputation

Median imputation

Mode imputation

Forward fill

2. Which of these is a best practice in data cleaning workflows?

Document all cleaning steps and transformations applied

Clean data without keeping records of changes

Remove all data points that seem unusual

Avoid creating backup versions of raw data

Submit

4. What is whitespace trimming in data cleaning?

Removing extra spaces from text fields

Deleting blank rows

Converting text to lowercase

Removing special characters

5. True or False: Feature scaling is necessary before training most machine learning models.

True

False

Submit

7. Which technique handles missing values by using predictions from other features?

Simple deletion

Forward fill

Predictive imputation

Random imputation

8. What is the primary goal of exploratory data analysis (EDA) in the cleaning process?

To visualize final results

To understand data structure, distributions, and identify quality issues

To build predictive models

To remove all categorical variables

9. True or False: Outliers should always be removed from a dataset before analysis.

True

False

Submit

11. What is data cleaning?

The process of removing all data from a dataset

Detecting and correcting errors, inconsistencies, and missing values in raw data

Organizing data alphabetically

Deleting duplicate records only

12. What is data validation?

Checking if data meets required quality standards and constraints

Deleting invalid data automatically

Creating backups of raw data

Encrypting sensitive information

13. True or False: Data standardization and normalization are the same process.

True

False

Submit

15. Which of the following is NOT a common data quality issue?

Missing values

Inconsistent formatting

Extremely large datasets

Duplicate records

16. What does normalization do in data cleaning?

Converts all data to text format

Scales numerical features to a standard range, typically 0-1 or -1 to 1

Removes categorical variables

Identifies missing values

17. True or False: Duplicated records should always be removed during data cleaning.

True

False

Submit

19. What is an outlier in data analysis?

A data point that is significantly different from other observations

The first entry in a dataset

Any negative value

Data that cannot be cleaned

20. Which method is commonly used to handle missing values in a numerical column?

Mean imputation

Mode imputation

Deletion of the entire column

Ignoring missing values

General Data Science Beginner Data Cleaning Basics Quiz

1. Which imputation method replaces missing values with the most frequent value?

2.

What first name or nickname would you like us to use?

2. Which of these is a best practice in data cleaning workflows?

3. The Z-score method is commonly used to detect ____.

4. What is whitespace trimming in data cleaning?

5. True or False: Feature scaling is necessary before training most machine learning models.

6. Data ____ refers to identifying and correcting inaccurate or incomplete information.

7. Which technique handles missing values by using predictions from other features?

8. What is the primary goal of exploratory data analysis (EDA) in the cleaning process?

9. True or False: Outliers should always be removed from a dataset before analysis.

10. Inconsistent data types across a column (e.g., numbers stored as text) should be ____.

11. What is data cleaning?

12. What is data validation?

13. True or False: Data standardization and normalization are the same process.

14. The process of converting categorical variables into numerical format is called ____.

15. Which of the following is NOT a common data quality issue?

16. What does normalization do in data cleaning?

17. True or False: Duplicated records should always be removed during data cleaning.

18. Handling missing data by removing rows with any missing values is called ____.

19. What is an outlier in data analysis?

20. Which method is commonly used to handle missing values in a numerical column?