Principal Component Analysis Course
This course teaches Principal Component Analysis (PCA) for practical data analysis. Learners will clean and scale high-dimensional data, select key components, interpret results for business use, visualise findings clearly, and compare PCA with t-SNE and UMAP. Ideal for turning complex customer data into actionable segments and robust models ready for production.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain mastery in Principal Component Analysis to reduce data dimensions effectively. Cover data cleaning, preprocessing, feature selection, efficient PCA on big datasets, interpreting loadings, confident component selection, visualisation for modelling, and comparisons with t-SNE and UMAP for strong pipelines.
Elevify advantages
Develop skills
- Clean and prepare data for PCA using scaling, imputation, and encoding.
- Select best principal components with scree plots, variance checks, and parallel analysis.
- Interpret PCA loadings and rotations for meaningful business insights.
- Implement PCA in Python with scikit-learn for efficient data reduction.
- Compare PCA against t-SNE and UMAP to pick the best method.
Suggested summary
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