IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Dimensionality reduction is a critical technique in data science and machine learning, aimed at simplifying datasets while preserving essential information. Principal Component Analysis (PCA) is one of the most widely used methods for this purpose.

PCA works by transforming the original variables in a dataset into a new set of uncorrelated variables called principal components. These components are derived in such a way that the first few retain most of the variation present in the original data. By focusing on these principal components, PCA significantly reduces the number of dimensions while maintaining meaningful data characteristics, which can improve the efficiency of subsequent modeling processes and help to visualize complex datasets.

In contrast, normalization is a preprocessing step that adjusts the scale of features but does not reduce dimensionality. K-means clustering is an unsupervised learning method used for clustering data points into groups, which does not involve dimensionality reduction. Regression analysis focuses on modeling relationships between variables rather than reducing dimensions within a dataset. Hence, PCA stands out as the appropriate choice for effectively reducing dimensionality while retaining crucial information from the dataset.