MAS2008
Scientific computing

Lecturer:
Neil Strickland

Module information

Most module information will appear here. There is also a Blackboard page for the course, with a discussion board etc.

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.
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.

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

Lectures and labs

Week 1:	Introduction to VS Code, revision	Lecture	Lab	Test
Week 2:	`numpy` arrays, linear algebra and vectorization	Lecture	Lab	Test
Week 3:	Plotting with `matplotlib`	Lecture	Lab	Test
Week 4:	Solving differential equations	Lecture	Lab	Test
Week 5:	Integration and PDEs	Lecture	Lab	Test
Week 6:	The Python ecosystem	Lecture	Lab	Test
Week 7:	Version control, testing, debugging	Lecture	Lab	(no test)
Week 8:	Data analysis and curve fitting	Lecture	Lab	Test
Week 9:	Signal processing	Lecture	Lab	Test
Week 10:	3D plotting and animations	Lecture	Lab	Test

Assignments

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.
Assignment 1 on root-finding was due at the end of Week 6. Feedback is now available here. Individual marks should now be visible in Blackboard, and you should have received an annotated copy of your work by email.
Assignment 2 on dynamical systems was due at the end of week 8. Feedback is now available here. Individual marks should now be visible in Blackboard, and you should have received an annotated copy of your work by email.
Assignment 3 on exoplanets was due at the end of Week 11. Feedback is now available here. Individual marks should now be visible in Blackboard, and you should have received an annotated copy of your work by email.

MAS2008 Scientific computing Lecturer: Neil Strickland