Linear Algebra for Data Science Course
Master linear algebra for data science and BI. Build feature matrices, detect redundant fields, apply PCA, and interpret model weights to explain predictions, reduce risk, and turn customer data into clear, actionable business insights.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This Linear Algebra for Data Science Course gives you practical tools to build, understand, and improve predictive models. Learn how to construct feature matrices, apply linear models, interpret weights, detect redundancy, and use PCA for dimensionality reduction. You will also practice regularization, error analysis, and deployment checks, gaining skills you can apply immediately to real data projects.
Elevify advantages
Develop skills
- Build feature matrices: turn BI tables into clean X and y for modeling fast.
- 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, stabilize 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 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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Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can switch chapters and skip content I don't need.
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