Linear Algebra
Why?
Linear Algebra is a necessary branch of math for data science. Data scientists work with datasets, so matrices are the way to express such lists of numbers, and this course will teach us how to handle & manipulate these structures. Especially for machine learning, linear algebra is considered a fundamental prerequisite, also you will use some of it in other future courses like data mining, social network analysis, advanced topics in data science...
What?
This course introduces matrices in its different types, its operations, inverse, determinants, and afterwards utilizes those to solve system of equations, decompose matrices, and find eigenvectors. Students will develop the mathematical foundation needed for advanced data science applications.
Curriculum:
Matrices
Define matrices and how to apply operations on them & find an inverse.
Determinants
Learn how to calculate determinants and understand their properties and applications.
Systems of Linear Equations
Solve linear systems using matrix methods and understand solution spaces.
Spectral Theory
Learn about eigenvalues, eigenvectors, and their applications in dimensionality reduction.
Vector Space
Foundations of vector spaces, linear independence, basis, dimension, orthogonality, and their relevance to data representation.
Notes
Linear Algebra's notation is new for most students, so some practice is necessary to feel comfortable & quick with it, as it is used heavily in machine learning and AI.
