Principal Component Analysis Course
Dive into Principal Component Analysis for practical statistics in Uganda contexts. Learn to clean and scale data, pick key components, interpret loadings, and stack up against t-SNE and UMAP. Transform complex customer data into simple segments, vivid visuals, and powerful prediction models ready for real use.

flexible workload from 4 to 360h
valid certificate in your country
What will I learn?
Gain expertise in reducing data dimensions using Principal Component Analysis. Master data cleaning, preprocessing, feature selection, and efficient PCA on big datasets. Understand loadings, select components confidently, 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 with scaling, filling gaps, and handling categories.
- Pick best principal components via scree plots, variance checks, and parallel analysis.
- Decode PCA loadings and rotations for straightforward business insights.
- Use Python and scikit-learn to apply PCA swiftly on large-scale data.
- Weigh PCA against t-SNE and UMAP to select ideal reduction techniques.
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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