MAS2008
Scientific computing

Lecturer:
Neil Strickland

I am no longer teaching this course but have left some material in place in case anyone finds it useful.

Arrangements

There will be one lecture and one lab session each week.
In lectures, I will often write code live and talk about it. I may also distribute tidier and more complete versions of that code, possibly with associated YouTube videos.
In the lab sessions, you will be asked to complete various tasks, which will partially overlap with tasks covered in the lecture.
For some of these tasks, you will be asked to paste code into an online test system. These tests will count for 10% of your grade for the course. There is a page of general instructions which you should read before attempting any tests.
There will be three assignments, each of which will account for 30% of your overall grade.
For each assignment, you will be asked to submit a Jupyter notebook containing a mixture of Python code and text with mathematics written in LaTeX (via Markdown).
Most of the credit will be for intelligent and accurate analysis of relevant mathematical issues and how they interact with software issues. Only a smaller part of the credit will be for the code itself.
You are welcome to send me email with questions, but you should first read the page asking for help for advice on how to ask questions effectively.

Setting up the environment

We will use Visual Studio Code as the main way to interact with Python. This is already installed on the university open access PCs, but you need to carry out a couple of small steps before you can use it. You can also install VS Code and other required software on your own PC or laptop. Instructions are given below:

University PC setup:	Web page	YouTube video
Personal PC setup:	Web page	YouTube video
Personal Mac setup:	Web page

Interactive demonstrations

There is a set of interactive demonstrations explaining some of the ideas in the course. I will talk through these in lectures, there are attached YouTube videos, and you can try them yourself at other times. These are experimental and under development. I welcome comments about the extent to which they are comprehensible, useful or interesting.

numpy broadcasting rules

Numerical integration

The Runge-Kutta method

Laplace's partial differential equation

pandas selection rules

Coordinates for the torus

Lectures and labs

Details of the lectures and labs will appear here as the course progresses.

Assignments

There will be three assignments, each counting for 30% of your overall grade. Details of the assignments will appear here as the course progresses.

There is a sample assignment on the Lotka-Volterra model with a sample solution which you can use as a guide to the kind of thing you should submit.