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 of models, feature engineering, and performance monitoring.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This Linear Algebra for Data Science Course equips you with practical tools to construct, comprehend, and enhance predictive models. You will learn to build feature matrices, implement linear models, analyse weights, spot redundancy, and employ PCA for dimensionality reduction. Additionally, you will engage in regularization, error assessment, and deployment verification, acquiring skills applicable right away to actual data initiatives.
Elevify advantages
Develop skills
- Build feature matrices: convert BI tables into neat X and y for swift modelling.
- Model with matrices: formulate, fit, and analyse linear models for BI KPIs.
- Detect multicollinearity: utilise XᵀX, VIF, and SVD to identify redundant BI features.
- Apply PCA in BI: diminish dimensions, steady models, and accelerate training.
- Monitor models in production: observe drift, retrain, and clarify 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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