MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with 10 million rows. The dataset has a column 'income' with outliers. The data scientist wants to detect and handle outliers. Which THREE approaches are appropriate?
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
✓
Calculate z-scores and flag values beyond 3 standard deviations
The correct approaches for detecting outliers in a dataset with 10 million rows are calculating z-scores (A), using IQR (E), and visualization with box plots (D). Z-scores flag values beyond 3 standard deviations, IQR identifies outliers as points below Q1-1.5*IQR or above Q3+1.5*IQR, and box plots provide a visual summary of the distribution. Min-max scaling (B) only transforms the data range and does not detect outliers. One-hot encoding (C) is for categorical variables, not outlier detection. Thus options A, D, and E are correct.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Calculate z-scores and flag values beyond 3 standard deviations
Why this is correct
Z-score is a common method.
- ✗
Apply min-max scaling to the column
Why it's wrong here
Scaling does not detect outliers.
- ✗
Convert the column to one-hot encoding
Why it's wrong here
One-hot encoding is for categorical data.
- ✓
Visualize the distribution with box plots
Why this is correct
Box plots help identify outliers visually.
- ✓
Use the interquartile range (IQR) to identify outliers
Why this is correct
IQR method is robust for outlier detection.
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