MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a high-dimensional dataset with 500 features. The scientist wants to visualize the data in 2D to check for clusters. Which dimensionality reduction technique should the scientist use that preserves global structure and is computationally efficient for large datasets?
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
✓
PCA
PCA is a linear dimensionality reduction technique that preserves global structure (variance) and is computationally efficient for large datasets. Option A is wrong because t-SNE is non-linear, slower, and focuses on preserving local structure, not global. Option B is wrong because LDA is a supervised technique that requires class labels, and it is not typically used for unsupervised exploration of clusters. Option D is wrong because UMAP is non-linear and can be slower than PCA; while it preserves both local and global structure to some extent, it is not as computationally efficient as PCA for very large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
t-SNE
Why it's wrong here
t-SNE is computationally expensive and preserves local structure, not global.
- ✗
Linear Discriminant Analysis (LDA)
Why it's wrong here
LDA requires class labels and is supervised.
- ✓
PCA
Why this is correct
PCA is linear, fast, and preserves global variance.
- ✗
UMAP
Why it's wrong here
UMAP may be faster than t-SNE but still non-linear and focuses on local structure.
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