Linear Algebra for Data Science with Python

Linear algebra is the hidden engine behind modern data science, powering everything from recommendation systems to deep learning models. Understanding these mathematical principles is essential for anyone looking to move beyond just running code and start truly understanding how algorithms work. This course bridges the gap between abstract math and practical programming. You will learn to translate linear algebra theory into efficient Python code, gaining the intuition needed to handle high-dimensional data and optimize machine learning workflows. What you'll learn: - Understand core concepts like vectors, matrices, and tensors in a data science context - Perform matrix operations and transformations using modern Python libraries - Apply linear algebra techniques to solve systems of linear equations - Explore dimensionality reduction concepts such as Principal Component Analysis - Practice vectorization to write faster and more efficient data processing code - Implement foundational algorithms that rely on matrix factorization and eigenvalues The course begins with fundamental definitions and notation before progressing to practical implementation and real-world data applications. You will work through written explanations and code-based exercises designed to solidify your mathematical intuition. This course is designed for beginners in data science or programming who want to master the mathematical side of the field; no prior experience with advanced math is required. Start building your mathematical foundation for a career in data science today.