Computational Physics – Python

Lecture Series, Guru Nanak Dev University (October 2025)

This repository contains lecture notes and Jupyter notebooks from the Computational Physics with Python lecture series conducted at Guru Nanak Dev University (GNDU) in October 2025. The course introduces computational methods in physics using Python, with applications ranging from basic programming to astronomical data analysis.

The material is intended for undergraduate and postgraduate students in physics and related disciplines, and emphasizes reproducible research practices and real-world scientific datasets.

Resources

Jupyter Notebooks: https://github.com/jsdingra11/gndu_python2025

Sloan Digital Sky Survey (SDSS) SkyServer DR18: https://skyserver.sdss.org/dr18/Visual/

Lecture Contents

Day 1 – Python and Version Control

• Version control systems
• Python basics: variables, data types, operators
• Input and output

Day 2 – Core Python Programming

• Lists, tuples, and dictionaries
• Indexing and slicing
• Functions (basics)

Day 3 – Object Oriented Programming

• Classes and objects
• Object oriented programming with physics-based examples

Day 4 – Scientific Python Libraries

• NumPy
• Pandas
• Basic Matplotlib

Day 5 – Astrophysical Data Analysis

• Gaussian profiles and multi-Gaussian models
• Redshift estimation of SDSS galaxies using Gaussian fitting

Scope and Objectives

The objective of this lecture series is to introduce students to computational approaches in physics using Python. The course covers numerical methods, data handling, visualization, and object-oriented design, and applies these tools to real astronomical datasets from the Sloan Digital Sky Survey. The repository is designed to serve as a reference for students and researchers interested in computational physics and astrophysical data analysis.

Instructor

Jashanpreet Singh Dingra
Guru Nanak Dev University
astrodingra@gamil.com