IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction, particularly in the field of data science and machine learning. The primary goal of PCA is to transform a large set of variables into a smaller set of uncorrelated variables called principal components while retaining as much variance in the data as possible. This transformation helps to diminish the number of dimensions of the dataset, making it easier to visualize and analyze, and it can also lead to improved performance of machine learning algorithms by mitigating the curse of dimensionality.

PCA works by identifying the directions (principal components) in which the data varies the most. By projecting the original data onto these components, one can reduce the complexity of the dataset. This is especially useful when dealing with high-dimensional data where many features may be redundant or irrelevant.

Other methods listed, such as data mining, linear regression, and cluster analysis, serve different purposes. Data mining encompasses a variety of techniques for extracting patterns and knowledge from large datasets but does not specifically focus on dimensionality reduction. Linear regression is primarily used for modeling relationships between input features and target variables, rather than reducing dimensionality. Cluster analysis is aimed at grouping similar data points together but does not inherently reduce the number of dimensions in the dataset.