This article presents a practical, beginner-to-intermediate guide on the most essential linear algebra concepts every data scientist must understand. It covers:
• Vectors and their use in representing data points and calculating similarity.
• Matrices, which are used for storing datasets and performing transformations.
• Linear equations that form the foundation of regression models.
• Eigenvalues, eigenvectors, and SVD, crucial for dimensionality reduction techniques like PCA.
• Concepts such as orthogonality, projections, and vector spaces, which support optimization and machine learning algorithms.
Each section connects the theory directly to its application in machine learning, data processing, NLP, and deep learning — making it both educational and actionable for real-world data science projects.
