This course introduces computational mathematics using Python, focusing on visualization and linear algebra. Students learn to plot elementary functions, plane curves, space curves, and three dimensional surfaces with clear labeling. The course develops matrix computation skills, including transpose, determinant, trace, norm, and inverse. Various methods for solving systems of linear equations are implemented, such as matrix inversion, Cramer's rule, Gaussian elimination, Gauss Jordan, and iterative techniques like Jacobi and Gauss Seidel. Students also compute dominant eigenvalues and eigenvectors using the Power Method, building practical numerical problem solving and programming competence. This course emphasizes accuracy, interpretation, efficiency, and clear computational thinking.
- Teacher: Sri. Bhargava K.