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 benchmark against t-SNE and UMAP. Transform complex high-dimensional customer data into actionable segments, compelling visualisations, and enhanced predictive models for real-world applications.

4 to 360 hours of flexible workload
certificate valid 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 large datasets. Interpret loadings confidently, select optimal components, and produce insightful visualisations for segmentation and modelling. Compare PCA against t-SNE and UMAP while building reliable production pipelines.
Elevify Advantages
Develop Skills
- Clean and encode data robustly for PCA, including scaling, imputation, and categorical handling.
- Select optimal principal components using scree plots, variance explained, and parallel analysis.
- Interpret PCA loadings and rotations to uncover business-relevant factors clearly.
- Implement PCA in Python using scikit-learn for efficient, scalable dimensionality reduction.
- Compare PCA with t-SNE and UMAP to select the most suitable method for your needs.
Suggested Summary
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