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Linear Algebra for Data Science Course

Linear Algebra for Data Science Course
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.
Workload: between 4 and 360 hours

What our students say

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Great course. A lot of valuable information.
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