Linear Algebra for Data Science Course
This course equips data science practitioners with essential linear algebra techniques tailored for building and deploying predictive models in business intelligence contexts, focusing on practical applications like feature engineering, model interpretation, and dimensionality reduction.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This Linear Algebra for Data Science Course provides practical tools to build, understand, and enhance predictive models. You will learn to construct feature matrices, apply linear models, interpret weights, detect redundancy, and utilise PCA for dimensionality reduction. Additionally, you will practise regularization, error analysis, and deployment checks, acquiring skills applicable 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 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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