This repository contains a collection of Jupyter notebooks and Python scripts that explore foundational techniques in numerical computational problem-solving.
The materials cover a variety of topics related to:
- Working with numerical data
- Error analysis and floating-point behavior
- Root-finding and optimization techniques
- Function interpolation and approximation
- Numerical differentiation and integration
- Solving ordinary differential equations (initial and boundary value problems)
- Concepts in linear algebra including decomposition methods and matrix computations
These notebooks are structured to build practical skills for modeling, analyzing, and solving computational problems using Python-based tools and libraries like NumPy, SciPy, and Matplotlib.
All examples emphasize clarity, reproducibility, and interpretation of results. This work serves as both a technical reference and hands-on coding practice for anyone interested in applying numerical methods to real-world problems.
- Python
- Jupyter Notebooks
- NumPy
- SciPy
- Matplotlib
This repository is intended for educational and reference purposes. All code and explanations are shared in the spirit of learning and knowledge building.