Linear Algebra for Data Science Course
This course provides essential linear algebra techniques tailored for data science applications in business intelligence, focusing on practical implementation for model building and analysis.

flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
This Linear Algebra for Data Science Course equips you with practical tools to build, understand, and enhance predictive models. You will learn to construct feature matrices, apply linear models, interpret weights, spot redundancy, and utilise PCA for dimensionality reduction. Additionally, you will practise regularization, error analysis, and deployment checks, acquiring skills to apply right away to real data projects.
Elevify advantages
Develop skills
- Build feature matrices: transform BI tables into clean X and y for modelling quickly.
- Model with matrices: express, fit, and interpret linear models for BI KPIs.
- Detect multicollinearity: use XᵀX, VIF, and SVD to identify redundant BI features.
- Apply PCA in BI: reduce dimensions, stabilise models, and accelerate training.
- Monitor models in production: track drift, retrain, and explain results to stakeholders.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workloadWhat our students say
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