| Course Name | Scientific Computing |
| Course Code | 26DS633 |
| Program | M Tech in Data Science |
| Semester | 2 |
| Credits | 3 |
| Campus | Coimbatore, Bengaluru |
Introduction to Mathematical Modelling, linear systems of equations, eigenvalues and eigenvectors, stability of systems using eigenvalue analysis, Taylor series, numerical differentiation, numerical integration, and higher-order accuracy schemes for integration and differentiation.
Ordinary Differential Equations (ODEs), Partial Differential Equations (PDEs), iterative methods, numerical methods including error analysis, stability and convergence, and Runge–Kutta methods.
Data-driven modelling, Dynamic Mode Decomposition (DMD), Koopman Theory, system identification techniques for nonlinear systems, and Physics-Informed Neural Networks.
Course Objectives
This course helps students understand established conventional computational techniques.
This course helps students understand the concepts related to probability, statistics, linear algebra and partial differential equations, as well as the methods used to solve these equations.
This course aims to help students implement computational methods using MATLAB and Python.
Course Outcomes
After completing this course, the students will be able to,
CO1: Develop mathematical models to describe natural/engineering/physical systems.
CO2: Solve differential equations using numerical schemes.
CO3: Analyze the stability of systems using dynamical systems theory.
CO4: Develop data-driven models of different engineering/natural/physical systems.
CO-PO Mapping
|
PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PSO1 |
PSO2 |
PSO3 |
|
CO |
|||||||||
|
CO1 |
3 |
3 |
2 |
2 |
– |
– |
3 |
1 |
2 |
|
CO2 |
3 |
3 |
3 |
2 |
– |
– |
3 |
1 |
2 |
|
CO3 |
3 |
3 |
2 |
3 |
1 |
– |
3 |
2 |
2 |
|
CO4 |
2 |
3 |
3 |
3 |
1 |
1 |
3 |
3 |
3 |
|
Assessment component |
Internal / External |
Suggested Assessment Methods |
Weightage (%) |
|
Continuous Assessment – Theory (CAT) |
Internal |
Problem-solving assignments, quizzes (conceptual & numerical), short tests (open/closed book), analytical exercises |
20% |
|
Continuous Assessment – Lab (CAL) |
Internal |
Computational exercises using Python/MATLAB, mini computational tasks |
30% |
|
Mid-Term Examination |
Internal |
Written examination focusing on analytical problem solving and modelling |
20% |
|
End Semester Assessment |
External |
Term Project / Hybrid mode: Written exam + problem-solving/modelling questions; may include a small computational component depending on course delivery |
30% |
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