Scientific Computing

Academic (University) preparation with worked examples, written practice and independent learning assessments.

Independent university-level study of Scientific Computing. This original curriculum draws on public academic subject descriptions; it is not an official faculty syllabus, university credit, accreditation or an Ivy League qualification. Topics covered: Floating-point arithmetic; Conditioning; Stability; Error propagation. Root finding; Fixed-point iteration; Newton methods; Convergence rates. Linear systems; Pivoting; Iterative solvers; Preconditioning. Interpolation; Splines; Least squares; Approximation error. Quadrature; Adaptive integration; Gaussian rules; Error estimation. ODE solvers; Stiffness; Adaptive timesteps; Stability regions. Eigenvalue algorithms; Power iteration; QR iteration; Singular values. Reproducible computation; Verification; Convergence studies; Scientific simulation project. Projects use safe simulations, public datasets or textual analysis; specialist subject review is required. Includes lessons, supplementary YouTube tutorials, practical exercises, quizzes and free/paid practice tests. Erudex is independent, not affiliated with the source institution or awarding body. Its 80% learning checks are not official grades or university credit. Content requires qualified subject review before use as a complete teaching programme.