Numerical linear algebra with emphasis on computational methods for solving matrix problems arising in scientific computing and data-driven applications, Matrix factorizations, least squares, singular value decomposition, conditioning, eigenvalue problems, iterative methods, efficient implementation and practical use in high-dimensional settings, including applications in data analysis and machine learning.