Linear Algebra for Data Science Course
This course equips data science practitioners with essential linear algebra techniques tailored for business intelligence and predictive modeling, focusing on practical applications like feature engineering, model interpretation, and dimensionality reduction to enhance model performance and deployment.

from 4 to 360h flexible workload
certificate valid in your country
What will I learn?
This Linear Algebra for Data Science Course provides practical tools to build, understand, and enhance predictive models. Learn to construct feature matrices, apply linear models, interpret weights, detect redundancy, and use PCA for dimensionality reduction. You will also practise regularization, error analysis, and deployment checks, gaining skills you can apply right away to real data projects.
Elevify advantages
Develop skills
- Build feature matrices: turn 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 find redundant BI features.
- Apply PCA in BI: reduce dimensions, stabilise models, and speed up 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 workload.What our students say
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