# Dimensionality Reduction

Skill · Data Science, Analytics and AI/ML

Canonical page: https://career.thegoodapps.co/skills/dimensionality-reduction

Dimensionality reduction is a set of techniques in machine learning and statistics used to reduce the number of variables in a dataset while preserving as much meaningful information as possible, using methods like Principal Component Analysis (PCA), t-SNE, or UMAP. Data scientists use it to simplify complex datasets, speed up model training, reduce noise, and visualize high-dimensional data in two or three dimensions.

Related skills: [Feature Engineering](https://career.thegoodapps.co/skills/feature-engineering), [Machine Learning](https://career.thegoodapps.co/skills/machine-learning), [Feature Selection](https://career.thegoodapps.co/skills/feature-selection), [Principal Component Analysis](https://career.thegoodapps.co/skills/principal-component-analysis), [Autoencoders](https://career.thegoodapps.co/skills/autoencoders), [Unsupervised Learning](https://career.thegoodapps.co/skills/unsupervised-learning)

## Open roles requiring Dimensionality Reduction (0)

None of the roles we have read name this skill yet. A large share of the visible corpus has not been parsed for skills, so this is at least as likely to be our backlog as the market's verdict.