DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset and finds that the 'age' column has several missing values. Which of the following is a valid method for handling missing numerical data?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Impute with the mean
Mean imputation is a common method for handling missing numerical data, though median or mode can also be used.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete the entire column
Why it's wrong here
Deleting entire column loses all data, not recommended if only some values missing.
- ✗
Ignore the missing values
Why it's wrong here
Ignoring may cause errors in analysis.
- ✓
Impute with the mean
Why this is correct
Correct: mean imputation is a standard technique.
- ✗
Replace with zeros
Why it's wrong here
Replacing with zeros can bias the analysis.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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