4. How do I reinforce learning after completing the course?

Practice with real-world datasets using regression, eigen-decomposition, and feature transformations.
Read practical linear algebra books like Practical Linear Algebra for Data Science for coding insights.
Try implementing PCA, least squares fitting, or clustering algorithms using Python or R.
Work through project-based tutorials that apply linear algebra to NLP, image compression, or graph analytics.
Join data science community challenges that require foundational math—reinforces both the why and how.