Computational methods

This course introduces Python programming for scientific and engineering applications. Students will develop problem-solving skills using computational tools, focusing on numerical methods, data analysis, and visualization. The course covers core Python programming concepts along with key scientific libraries NumPy, SciPy, and Matplotlib, enabling students to model, analyze, and visualize engineering systems.

Objectives

By the end of the course, students will be able to:

• Develop structured Python programs to solve engineering problems.
• Apply numerical methods using scientific libraries for data analysis and modeling.
• Manipulate and analyze datasets efficiently using array-based computing.
• Visualize engineering data and simulation results effectively.

Contents

Unit I: Fundamentals of Python programming

1. Basic syntax, variables, and data types
2. Control flow structures
3. Functions and modular programming
4. File input/output and basic debugging

Unit II: Numerical computing with NumPy

5. Introduction to arrays and vectorized operations
6. Array indexing, slicing, and broadcasting
7. Linear algebra operations
8. Performance considerations and vectorization

Unit III: Scientific computing with SciPy

9. Numerical integration and differentiation
10. Solving linear and nonlinear equations
11. Optimization techniques
12. Introduction to ordinary differential equations (ODEs)

Unit IV: Data visualization with Matplotlib

13. Basic plotting
14. Customization of plots
15. Multi-plot figures and subplots
16. Visualization of scientific and engineering data

Lectures