A collection of Python exercises, scientific computing examples, and introductory data analysis projects developed while building practical programming skills for computational research.
This repository documents my progression in Python, with an emphasis on scientific computing, numerical methods, data analysis, visualization, and introductory machine learning.
The projects range from Python fundamentals to NumPy, SciPy, Matplotlib, regression analysis, and basic computer vision.
- Python programming fundamentals
- Object-oriented programming
- NumPy and numerical computing
- Matrix and vector operations
- Matplotlib and data visualization
- SciPy and numerical methods
- Linear and polynomial regression
- Data analysis
- Basic computer vision
- Jupyter Notebook workflows
python-scientific-computing/
│
├── python-basics/
├── numpy/
├── matplotlib/
├── scipy/
├── machine-learning/
├── computer-vision/
└── datasets/
- Python
- NumPy
- SciPy
- Matplotlib
- Jupyter Notebook
- Pandas
- scikit-learn
Examples involving matrix operations, matrix inversion, vector calculations, and numerical algorithms using NumPy.
A collection of Jupyter notebooks exploring data visualization and plotting techniques with Matplotlib.
Introductory implementations and experiments involving:
- Simple linear regression
- Multiple linear regression
- Polynomial regression
Basic image-processing and face-detection experiments using Python.
Ali Tavahodi
M.Sc. in Condensed Matter Physics