Numerical Methods
Numerical analysis and scientific computing — bridging theoretical mathematics with computational implementation.
Tools: Python, MATLAB, SciPy, NumPy
Topics covered
- Root-finding: Newton-Raphson, bisection, secant method
- Numerical integration: quadrature rules, Runge-Kutta for PDE
- Matrix decompositions: LU, QR, SVD, Cholesky
- Finite difference and finite element methods
- Error analysis, numerical stability, and condition numbers
- Interpolation: polynomial, spline, Lagrange
Completed Simulation and Modelling of Natural Processes (University of Geneva) covering Python and MATLAB implementations across all of the above.
Related: Mathematics, Physics, SciPy, PDE, Optimization