jrjohansson/scientific-python-lectures

This repository is a set of lecture notes for learning scientific computing with Python. The material is packaged as interactive notebooks that you can open and run on your own computer, letting you read explanations and execute code examples in the same document. The lectures are organized into numbered topics. The first covers an overview of scientific computing and why Python is used for it. The second is an introduction to Python programming itself. After that the series moves into the main scientific libraries: Numpy for working with arrays and numerical data, Scipy for scientific algorithms such as integration, optimization, and signal processing, and Matplotlib for creating 2D and 3D plots. Later lectures cover Sympy for symbolic algebra, integration with compiled languages like C and Fortran when performance matters, high-performance computing techniques, and version control with software like Git. To use the material, you download the files and open them with Jupyter, a tool that lets you run Python code inside a web browser. The README includes a short command for starting the notebook server locally. A compiled PDF of all lectures is also available for those who prefer a static document. The license is Creative Commons Attribution 3.0, meaning the material can be reused and shared freely as long as the original author is credited. The repository is not a software tool but a learning resource, and the notebooks are read as educational content rather than run as an application. It is well suited for someone who wants a structured introduction to using Python for numerical and scientific work.