Who Is Pca

Principal component analysis (pca) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. Students who complete the pca plus. Principal component analysis, or pca, reduces the number of dimensions in large datasets to principal components that retain most of the original information.

Jun 23, 2025principal component analysis (pca) is a statistical technique that simplifies complex data sets by reducing the number of variables while retaining key information. Principal component analysis (pca) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. Principal component analysis (pca) is a foundational linear dimensionality reduction technique widely used in statistics, data science, and machine learning (ml).

The pca plus workforce training program combines coursework and lab skills training over 4 weeks, totaling 54 hours of training overall.