Principal Component Analysis Course
This course equips you to master Principal Component Analysis for practical data analysis. You'll clean and scale high-dimensional datasets, select optimal components, interpret loadings for insights, and build visualisations for segmentation and modelling. Compare PCA with t-SNE and UMAP to create robust pipelines that turn complex data into actionable business intelligence and stronger predictions.

from 4 to 360h flexible workload
certificate valid in your country
What will I learn?
Gain expertise in Principal Component Analysis to reduce data dimensions effectively. Master data cleaning, preprocessing, feature selection, and efficient PCA on large datasets. Learn to interpret loadings, select components confidently, visualise results for segmentation and modelling, and compare with t-SNE and UMAP for production-ready pipelines.
Elevify advantages
Develop skills
- Clean and preprocess data for PCA using scaling, imputation, and encoding.
- Select key principal components via scree plots, variance explained, and parallel analysis.
- Interpret PCA loadings and rotations to uncover meaningful business factors.
- Implement PCA in Python with scikit-learn for efficient dimensionality reduction.
- Compare PCA against t-SNE and UMAP to pick the best method for your data.
Suggested summary
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