MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with 1 million rows and 50 features. The dataset includes a column 'user_id' with unique identifiers, a column 'event_date' with timestamps, and other columns. Which TWO actions should the data scientist take to understand data quality issues?
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
✓
Analyze missing value patterns across columns
Analyzing missing value patterns (A) is a fundamental EDA step to identify data quality issues such as incomplete records. Checking for duplicate rows based on 'user_id' and 'event_date' (B) helps ensure data integrity, as duplicates can skew analysis. Option C (dropping 'user_id') is premature; identifier columns can be useful for deduplication and merging. Option D (PCA) is a dimensionality reduction technique used later, not for initial data quality checks. Option E (training a model) is part of modeling, not EDA.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Analyze missing value patterns across columns
Why this is correct
Missing value analysis is key for data quality.
- ✓
Check for duplicate rows based on 'user_id' and 'event_date'
Why this is correct
Duplicates can indicate data quality issues.
- ✗
Drop the 'user_id' column to reduce dimensionality
Why it's wrong here
Should not drop columns before analysis.
- ✗
Use PCA to reduce dimensions and visualize
Why it's wrong here
PCA is for dimensionality reduction, not data quality.
- ✗
Train a random forest model to identify feature importance
Why it's wrong here
Model training is not part of EDA.
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