# Principal Component Analysis

Skill · Data Science, Analytics and AI/ML

Canonical page: https://career.thegoodapps.co/skills/principal-component-analysis

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of large datasets by transforming correlated variables into a smaller set of uncorrelated variables called principal components, which capture the most variance in the data. It's widely used by data scientists and researchers for data visualization, noise reduction, and as a preprocessing step before machine learning modeling. PCA is a foundational tool in fields like genomics, finance, and image processing.

Related skills: [Feature Engineering](https://career.thegoodapps.co/skills/feature-engineering), [Dimensionality Reduction](https://career.thegoodapps.co/skills/dimensionality-reduction), [Linear Algebra](https://career.thegoodapps.co/skills/linear-algebra), [Multivariate Analysis](https://career.thegoodapps.co/skills/multivariate-analysis), [Unsupervised Machine Learning](https://career.thegoodapps.co/skills/unsupervised-machine-learning)

## Open roles requiring Principal Component Analysis (1)

- [Industrial R&D Statistics & Experimentation Manager](https://career.thegoodapps.co/jobs/2ba37be5-2ea1-4fb2-85cf-56570e9e5eda/industrial-r-d-statistics-experimentation-manager-at-general-mills) at [General Mills](https://career.thegoodapps.co/organizations/general-mills) — Full-time, Minneapolis, MN, $128,900 – $193,400