MLS-C01 Exploratory Data Analysis Practice Question
Which THREE actions are valid steps in exploratory data analysis when working with a new dataset? (Choose three.)
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
✓
Check the data types of each column.
Options A, B, and E are correct. A: Checking data types is fundamental in EDA to understand the nature of each variable. B: Generating descriptive statistics (mean, std, min, max) provides a quick summary of central tendency, dispersion, and range for numerical features. E: Creating histograms helps visualize the distribution of numerical features, revealing skewness, outliers, or patterns. Option C is incorrect because fitting a linear regression model is a modeling step, not part of EDA. Option D is incorrect because splitting the dataset into training and test sets is for model validation, not for initial data exploration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Check the data types of each column.
Why this is correct
Understanding data types is essential.
- ✓
Generate descriptive statistics (mean, std, min, max).
Why this is correct
Descriptive statistics summarize central tendency and spread.
- ✗
Fit a linear regression model to identify important features.
Why it's wrong here
Model fitting is beyond EDA.
- ✗
Split the dataset into training and test sets.
Why it's wrong here
Splitting is part of modeling, not EDA.
- ✓
Create histograms for numerical features.
Why this is correct
Histograms visualize distributions.
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