Scientific Computing Methods for Data Science (MA725)

Course Name: 

Scientific Computing Methods for Data Science


M.Tech (CDS)




Programme Core (PC)

Credits (L-T-P): 



System of Linear Systems & Linear Least-Squares: Special types of matrices: Symmetric, Positive definite, Banded,
Dense and Sparse matrices; Diagonal dominance, Matrix and vector norms, Condition numbers; Matrix Decomposition
Methods: LU decomposition, QR factorisation, Singular value decompositions; Basic iterative methods for linear
systems: Jacobi, Gauss-Seidel and successive over relaxation methods; Projection Methods - Steepest descent, Minimal
residual iteration, Residual Norm Steepest descent.
System of Nonlinear Equations: Newton Method, Broyden’s method (secant type).
Numerical Differentiation: Finite differences, Richardson extrapolation, Automatic differentiation; Numerical
Integration: Gauss quadrature methods, Multiple integrals, Monte-Carlo integration methods.
Methods for Ordinary Differential Equations: Initial value problems: Euler and backward Euler methods, Runge-Kutta
methods, Applications to Stiff differential equations, Linear multi-step methods /predictor-corrector methods; Boundary
value problems: Shooting method, Finite difference methods, Finite element method - Galerkin method.


R. L. Burden and J. D. Faires, Numerical Analysis, 9th Edn, Brooks/Cole, 2010 Y. Saad, Iterative Methods for Sparse Linear Systems, 2nd Edn. 2000 G. W. Collins, II, Fundamental Numerical Methods for Data Analysis, 2003 Jain M. K., Numerical Solution of Differential Equations, New Age International Publishers, 3rd Edn, 2014 J. Solomon, Numerical Algorithms - Methods for Computer Vision, Machine Learning, and Graphics, CRC Press, 2015


Mathematical and Computational Sciences

Contact us

Dr. P. Sam Johnson, Professor and Head
Department of MACS, NITK, Surathkal
P. O. Srinivasnagar, Mangalore - 575 025
Karnataka, India.

  • Hot line: +91-0824-2474048

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