Mathematics for Data Science: Linear Algebra with MATLAB

Master the essential mathematical concepts for data science with our comprehensive Linear algebra MATLAB course. This course is designed to help students, engineers, and data scientists develop a solid understanding of linear algebra and apply it to real-world problems using MATLAB. Whether you’re working in AI, machine learning, or numerical computing, this course will equip you with the foundational tools needed for data-driven decision-making.

What You’ll Learn

• Fundamentals of vectors, matrices, and linear transformations
• Matrix operations and their applications in data science
• Solving systems of linear equations with MATLAB
• Eigenvalues, eigenvectors, and principal component analysis (PCA)
• Singular value decomposition (SVD) for dimensionality reduction
• Applications of linear algebra in machine learning
• MATLAB coding for numerical computations
• Optimization techniques using linear algebra

Requirements

• Basic understanding of algebra and calculus
• Familiarity with programming concepts (MATLAB experience is helpful but not required)
• Interest in data science, machine learning, or numerical computing

Course Description

This Linear algebra MATLAB course bridges the gap between mathematical theory and practical application. Linear algebra is a critical tool in data science, machine learning, and computational mathematics. MATLAB, one of the most widely used platforms for numerical computing, provides powerful built-in functions to handle complex linear algebra operations efficiently.

Throughout this course, you will gain a deep understanding of matrices, transformations, eigenvalues, and vector spaces. You will also apply these concepts in data science tasks such as dimensionality reduction, regression analysis, and machine learning model optimization. Hands-on exercises with MATLAB will help you reinforce your understanding and prepare you for real-world problem-solving.

By the end of this course, you will be able to confidently apply linear algebra techniques using MATLAB, optimize computational efficiency, and leverage mathematical models for data-driven applications.

About the Publication

This course is developed by an experienced data scientist and MATLAB expert with extensive knowledge of mathematical modeling, numerical computing, and machine learning. The structured approach ensures that both beginners and professionals can effectively grasp the core concepts and apply them in real-world scenarios.

Explore These Valuable Resources

• MathWorks: MATLAB for Machine Learning
• MIT OpenCourseWare: Linear Algebra
• Towards Data Science: Linear Algebra for Data Science

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