Principal Component Analysis Course
This course equips you with Principal Component Analysis skills for practical statistics. Learn to clean and scale data, select key components, interpret loadings accurately, and compare PCA against t-SNE and UMAP. Transform complex high-dimensional data, like customer datasets, into actionable segments, insightful visuals, and powerful predictive models ready for real-world use.
from 4 to 360h flexible workload
certificate valid in your country
What will I learn?
Gain expertise in dimensionality reduction through Principal Component Analysis. Master data cleaning, preprocessing, feature selection, and efficient PCA application on big datasets. Interpret loadings confidently, select components, visualise for segmentation and modelling, and compare 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 best principal components via scree plots, variance explained, and parallel analysis.
- Interpret PCA loadings and rotations to uncover meaningful business insights.
- Implement PCA in Python with scikit-learn for efficient dimensionality reduction.
- Evaluate PCA against t-SNE and UMAP to pick optimal methods.
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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