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