DA0-002 Data Analysis Practice Question
A data analyst is preparing a dataset for a machine learning algorithm that assumes normally distributed features. Which TWO data transformation methods should the analyst consider to achieve this?
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
✓
Square root transformation
Log transformation and square root transformation are both methods that can reduce skewness and help make data more normally distributed. Z-score standardization only centers and scales the data but does not change the shape of the distribution. One-hot encoding is for categorical variables, and min-max normalization is for scaling, not normality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Square root transformation
Why this is correct
Correct. Square root transformation can reduce right skewness and make the distribution more normal.
- ✓
Log transformation
Why this is correct
Correct. Log transformation is effective at reducing positive skewness, making data more normally distributed.
- ✗
One-hot encoding
Why it's wrong here
Incorrect. One-hot encoding is used for converting categorical variables into numerical form, not for achieving normality.
- ✗
Z-score standardization
Why it's wrong here
Incorrect. Z-score standardization standardizes the data to have mean 0 and standard deviation 1, but it does not alter the distribution shape and therefore does not make data normally distributed.
- ✗
Min-max normalization
Why it's wrong here
Incorrect. Min-max normalization scales data to a fixed range, but it does not affect distribution shape.
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