MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with a mix of numerical and categorical features. The target variable is binary. The data scientist wants to visualize the distribution of a numerical feature across the two target classes. Which TWO visualization techniques are appropriate? (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
✓
Overlapping histograms with transparency
Correct answers are options C and D. Option C (Overlapping histograms with transparency) is appropriate because it allows comparing the distribution of a numerical feature across two classes by overlaying histograms, making it easy to see shape, central tendency, and spread. Option D (Side-by-side boxplots) is appropriate because it succinctly displays median, quartiles, and outliers for each class, facilitating comparison. Option A (Heatmap of the correlation matrix) is not suitable as it visualizes correlations between features, not distribution of a single feature across classes. Option B (Stacked bar chart of the feature binned) is intended for categorical data, not numerical distributions. Option E (Scatter plot with color-coded classes) requires two numerical variables and is used to show relationships, not distribution of a single numerical feature.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Heatmap of the correlation matrix
Why it's wrong here
Heatmaps show correlations, not distributions.
- ✗
Stacked bar chart of the feature binned
Why it's wrong here
Bar charts are for categorical features.
- ✓
Overlapping histograms with transparency
Why this is correct
Histograms show distribution shapes; transparency allows comparison.
- ✓
Side-by-side boxplots
Why this is correct
Boxplots compare distributions across categories.
- ✗
Scatter plot with color-coded classes
Why it's wrong here
Scatter plots need two numerical variables.
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