Description: “Numerical Analysis and Computer Programming describes more advanced Engineering Mathematics topics which provide students with the relevant mathematical tools required in the analysis of problems in engineering and scientific professions and a part of Mathematics III. The topics covered include numerical analysis and computer programming. The mathematical skills derived from this course form a necessary base to analytical and design concepts encountered in the program.”
Curriculum
- 1 Section
- 38 Lessons
- 10 Weeks
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- Numerical Analysis38
- 2.1Lecture 1: Programing Basics
- 2.2Lecture 2: Introduction to Pointers
- 2.3Lecture 3: Pointers And Arrays
- 2.4Lecture 4: External Functions and Argument Passing
- 2.5Lecture 5: Representation of Numbers
- 2.6Lecture 6: Numerical Error
- 2.7Lecture 7: Error Propagation and Stability
- 2.8Lecture 8: Polynomial Interpolation I
- 2.9Lecture 9: Polynomial Interpolation II
- 2.10Lecture 10: Error In Interpolation Polynomial
- 2.11Lecture 11: Polynomial Interpolation
- 2.12Lecture 12: Cubic Spline Interpolation
- 2.13Lecture 13: Data Fitting Linear Fit I
- 2.14Lecture 14: Data Fitting Linear Fit II
- 2.15Lecture 15: Data Fitting Non Linear Fit
- 2.16Lecture 16: Matrix Elimation and Solution
- 2.17Lecture 17: Solution To Linear Equations
- 2.18Lecture 18: Matrix Elimination
- 2.19Lecture 19: Eigen Values of A Matrix
- 2.20Lecture 20: Eigen Values And Eigen Vectors
- 2.21Lecture 21: Solving NonLinear Equations
- 2.22Lecture 22: Solving NonLinear Equations Newton Rapson Method
- 2.23Lecture 23: Methods For Solving NonLinear Equations
- 2.24Lecture 24: System of NonLinear Equations
- 2.25Lecture 25: Numerical Derivations
- 2.26Lecture 26: High order Derivatives From Difference Formula
- 2.27Lecture 27: Numerical Integration Basic Rules
- 2.28Lecture 28: Comparison of Different Basic Rules
- 2.29Lecture 29: Gaussian Rules
- 2.30Lecture 30: Comparison of Gaussian Rules
- 2.31Lecture 31: Solving Ordinary Differential Equations I
- 2.32Lecture 32: Solving ordinary differential equations II
- 2.33Lecture 33: Adaptive step size Runge Kutta scheme
- 2.34Lecture 34: Partial Differential Equations
- 2.35Lecture 35: Explicit and Implicit Methods
- 2.36Lecture 36: The Crank Nicholson Scheme For Two Spatial
- 2.37Lecture 37: Fourier Transforms
- 2.38Lecture 38: Fast Fourier Transforms
