MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with 500,000 rows and 20 columns. The dataset contains missing values in some columns. Which TWO approaches are appropriate for handling missing data during EDA? (Choose 2)
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
✓
Create an indicator column to flag whether the value was missing, then impute with a placeholder
Options C and E are correct. Creating an indicator column to flag missingness and then imputing with a placeholder (e.g., mean/median) is a common technique to preserve information about missing patterns. Imputing numerical columns with median and categorical with mode is robust to outliers and preserves distribution. Option A (forward fill) is typically used for time series data, not general tabular EDA. Option B (listwise deletion) can reduce sample size and introduce bias if data is not missing completely at random. Option D (mean imputation) is sensitive to outliers and can distort variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use forward fill to propagate the last observed value
Why it's wrong here
Forward fill is appropriate for time series data, not general datasets.
- ✗
Remove all rows with any missing value (listwise deletion)
Why it's wrong here
Listwise deletion reduces sample size and can introduce bias.
- ✓
Create an indicator column to flag whether the value was missing, then impute with a placeholder
Why this is correct
This retains the information about missingness and is a common practice.
- ✗
Impute missing values with the mean of each column
Why it's wrong here
Mean imputation is sensitive to outliers and can distort distributions.
- ✓
Impute missing values with the median for numerical columns and mode for categorical columns
Why this is correct
Median and mode are robust to outliers and preserve the central tendency.
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