Principal Component Analysis Course
This course teaches Principal Component Analysis for practical data analysis. Participants learn to clean and scale high-dimensional data, select key components, interpret loadings for insights, and compare PCA to t-SNE and UMAP. Apply these skills to segment customer data, produce clear visualizations, and enhance predictive models in real-world scenarios.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain expertise in Principal Component Analysis for effective dimensionality reduction. Master data cleaning, preprocessing, feature selection, and efficient PCA on large datasets. Learn to interpret loadings, select components confidently, visualize results for segmentation and modeling, and compare with t-SNE and UMAP while building production-ready pipelines.
Elevify advantages
Develop skills
- Clean and encode data for PCA using robust scaling, imputation, and category handling.
- Select optimal principal components with scree plots, variance, and parallel analysis.
- Interpret PCA loadings and rotations to uncover business-ready factors.
- Apply PCA in Python with scikit-learn for scalable dimensionality reduction.
- Compare PCA with t-SNE and UMAP to select the best method.
Suggested summary
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