Principal Component Analysis Course
Dive into Principal Component Analysis for practical stats applications. Learn to clean and scale data, pick key components, interpret loadings accurately, and stack up against t-SNE and UMAP. Transform complex customer data into insightful segments, crisp visuals, and powerful predictive models ready for real use.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain expertise in dimensionality reduction via Principal Component Analysis. Master data cleaning, preprocessing, feature selection, and efficient PCA on big datasets. Interpret loadings confidently, select components, visualise for segmentation and modelling, and compare with t-SNE and UMAP for solid pipelines.
Elevify advantages
Develop skills
- Clean and prepare data for PCA using scaling, imputation, and category encoding.
- Choose best principal components via scree plots, variance checks, and parallel analysis.
- Decode PCA loadings and rotations for actionable business insights.
- Implement PCA in Python with scikit-learn for quick, scalable reductions.
- Evaluate PCA against t-SNE and UMAP to pick ideal methods.
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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Giulio CarloDigital Marketing Student
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