Principal Component Analysis Course
Master PCA for real-world statistics. Clean and scale data, select components, interpret loadings, and compare with t-SNE/UMAP. Turn high-dimensional customer data into clear segments, sharp visuals, and stronger predictive models.
from 4 to 360h flexible workload
certificate valid in your country
What will I learn?
Master dimensionality reduction with this focused Principal Component Analysis Course. Learn practical data cleaning and preprocessing, smart feature selection, and how to run PCA efficiently on large datasets. Interpret loadings, choose components with confidence, and create clear visualizations for segmentation and modeling. Compare PCA with t-SNE and UMAP, and build robust, production-ready pipelines.
Elevify advantages
Develop skills
- Clean and encode data for PCA: robust scaling, imputation, and category handling.
- Select optimal principal components using scree plots, variance, and parallel analysis.
- Interpret PCA loadings and rotations to reveal clear, business-ready factors.
- Apply PCA in Python with scikit-learn for fast, scalable dimensionality reduction.
- Compare PCA with t-SNE and UMAP to choose the right dimensionality method.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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