Principal Component Analysis Course
This course teaches Principal Component Analysis (PCA) for effective dimensionality reduction in data science. Participants will learn to clean and preprocess high-dimensional data, select optimal components using scree plots and variance analysis, interpret loadings for business insights, apply PCA with Python's scikit-learn on large datasets, and compare it with advanced methods like t-SNE and UMAP. Ideal for building robust pipelines that transform complex customer data into clear segments, insightful visualisations, and improved predictive models.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain expertise in Principal Component Analysis for dimensionality reduction. Master data cleaning, preprocessing, feature selection, and efficient PCA application on big datasets. Learn to interpret loadings, select components confidently, visualise results for segmentation and modelling, and compare PCA with t-SNE and UMAP to develop production-ready data pipelines.
Elevify advantages
Develop skills
- Clean and preprocess 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 factors.
- Implement PCA in Python with scikit-learn for efficient dimensionality reduction.
- Compare PCA against t-SNE and UMAP to pick suitable reduction methods.
Suggested summary
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