MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a dataset with both numerical and categorical features. The scientist wants to visualize the pairwise relationships between numerical features and also see the distribution of each feature. Which type of plot should the scientist use?
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
✓
Pair plot (scatter matrix) with histograms on the diagonal.
A pair plot (scatter matrix) shows pairwise scatter plots for numerical features and histograms on the diagonal for distribution of each feature. Option B is incorrect because a box plot shows distribution of a single feature, not pairwise relationships. Option C is incorrect because a heatmap of the correlation matrix shows only correlation values, not distributions or actual data points. Option D is incorrect because a correlation matrix with numbers only shows correlation coefficients, not individual feature distributions or pairwise scatter patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pair plot (scatter matrix) with histograms on the diagonal.
Why this is correct
Correct. Pair plots display scatter plots for every pair of numerical features and histograms on the diagonal to show each feature's distribution.
- ✗
Box plot for each feature.
Why it's wrong here
Incorrect. Box plots show summary statistics (median, quartiles, outliers) for a single feature, not pairwise relationships.
- ✗
Heatmap of the correlation matrix.
Why it's wrong here
Incorrect. A correlation heatmap visualizes the correlation matrix with colors, but does not show individual data point distributions or pairwise scatter plots.
- ✗
Correlation matrix with numbers.
Why it's wrong here
Incorrect. A numeric correlation matrix only provides correlation coefficients, without any visual distribution or scatter information.
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