Linear Algebra Foundations for Data Science in Python

Behind every powerful data science model and machine learning algorithm lies the mathematics of linear algebra. Understanding how vectors, matrices, and linear transformations work—and how to implement them in Python—is the crucial first step to unlocking a career in data science. This text-based course guides you from absolute beginner to confidently manipulating numerical data using Python. You will transition from theoretical math concepts to writing practical code, building the mental models needed to solve real-world analytical problems. What you'll learn: Understand foundational linear algebra terminology, including scalars, vectors, and matrices; Perform essential matrix operations such as addition, multiplication, and transposition using Python; Configure a modern Python development environment with virtual environments and type hints for mathematical code; Apply vector and matrix concepts to basic data representation and manipulation; Solve systems of linear equations using computational methods in Python; Practice translating mathematical formulas into clean, readable Python code. The course begins with core mathematical definitions and basic notation before walking you through hands-on code implementations. You will progress from simple vector operations to complex matrix transformations, learning how to leverage Python's mathematical ecosystem along the way. This course is designed for absolute beginners, aspiring data analysts, and curious professionals. No prior background in advanced mathematics or programming is required. Start building your mathematical foundation and write your first linear algebra code today.