Introduction to Scientific Computation Using Python

by Arun Prasaad Gunasekaran

Table of Contents

What is this playlist about?

  • A reference guide for people who wants to learn and use Python for scientific computation.
  • A personal initiative to address a lot of concepts and make programming feasible to a lot of students.

Why am I making this? - Part 1

  • To help people break their fear of programming.
  • To equip people with sufficient tools for their scientific or engineering or mathematical requirements.

Why am I making this? - Part 2

  • A challenge to myself to write and practice code for scientific computation.
  • An attempt for me to go back to the basics and re-emerge to the advanced concepts with a lot of applications.
  • To break my fear of failure.

Reference Books

For now, a majority of the ideas will be inspired from the two books below:

  • Computational Physics - Problem Solving with Python, Third Edition, Rubin H. Landau, Manuel J, Paez, and Cristian C. Boudeianu, Wiley Publications
  • A Primer on Scientific Programming with Python, Third Edition, Hans Petter Langtangen, Springer

Note about references

  • In the future, I’ll add more references and examples.
  • You need not have the references! :D. If you have it, then its a bonus!
  • I’ll provide any additional references if content were taken outside of these books.

Software Requirements

  • Must have a working installation of Python 3 installed in Windows/Linux/MacOS.

Personal Recommendation: Python from Anaconda/Miniconda or from Python from Enthought Canopy Distributions.

These distributions of python are designed specifically for scientific computation and have a collection of many commonly used packages and software at your disposal.

  • One good Interactive Development Environment (IDE) for coding.

Personal Recommendation: Spyder, Pycharm

My setup

  • Linux - Ubuntu 18.04 LTS 64-bit
  • Python - Anaconda Python Distribution - Python 3.7+
  • IDE - PyCharm


Ideology: Scientific Programming is an Amalgamation of Science, Mathematics, and Computer Science

We’ll be dealing with

  • Data Visualisation,
  • Making simple models,
  • Running simulations,
  • Numerical Experiments,
  • Analysis of Experimental data,
  • Inferences from the data

Final note before we get started,

  • Do experiment!
  • Point-out mistakes if I made!
  • Feel free to give suggestions (I’ll try my best to incorporate them)

Are you ready?

Let’s start then! :D