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 results for business insights, and compare PCA with t-SNE and UMAP. Apply these skills to segment customer data, produce clear visualisations, 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 to reduce data dimensions effectively. Cover data cleaning, preprocessing, feature selection, and efficient PCA on large datasets. Master interpreting loadings, selecting components confidently, visualising results for segmentation and modelling, and comparing with t-SNE and UMAP for robust pipelines.
Elevify advantages
Develop skills
- Clean and prepare data for PCA using scaling, imputation, and encoding techniques.
- Select the best principal components with scree plots, variance checks, and parallel analysis.
- Interpret PCA loadings and rotations to uncover meaningful business factors.
- Implement PCA in Python using scikit-learn for efficient data reduction.
- Compare PCA against t-SNE and UMAP to pick the ideal method.
Suggested summary
Before starting, you can change the chapters and the 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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