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MAT-20031 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2024/25 Last Updated: 21 November 2024

MAT-20031 - Computational Mathematics
Coordinator: Peter Wootton Tel: +44 1782 7 33767
Lecture Time: See Timetable...
Level: Level 5
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2024/25

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25

Scientific computing is one of the cornerstones of modern applied mathematics. By arming students with a high-level general purpose programming language ¿Python¿, they will be well equipped to explore a plethora of mathematical problems otherwise inaccessible to them. Moreover, students will possess a valuable, transferable skill, necessary in a large number of industries, for example, data science, engineering, meteorology and finance.
This module also will embed the use of technology to aid undergraduate mathematical studies.

Aims
¿The main aim of this module is to introduce students to the three elements of scientific computing; numerical analysis, programming and modelling.
In particular, the programming element aims to provide the students with a valuable transferrable skill in the Python programming language. We also aim to give the students a broad appreciation of the different computational tools at their disposal, and an introduction to numerical analysis.

Intended Learning Outcomes

choose and apply appropriate computational tools to help to solve and analyse a variety of problems: 1,2,3
create appropriate graphics (including interactive or animated) to illustrate a particular problem and/or solution: 1,2,3
write well commented and structured Python code with appropriate use of modules/libraries: 1,2,3
apply iterative methods to analyse and solve algebraic equations: 1,3
1,2,3
demonstrate the importance of the precision and bounds of floating point numbers: perform numerical integration, differentiation and interpolation, showing knowledge of numerical convergence: 2,3

Study hours

44 hours of lab/lecture sessions
2 hours of asynchronous on-demand videos
104 hours private study, including preparation of coursework/project

School Rules

None

Description of Module Assessment

1: Exercise weighted 20%
Programming exercises


2: Exercise weighted 20%
Numerical analysis exercises


3: Project weighted 60%
Individual study project


The University undertakes a continuous review of its teaching and research provision to ensure programmes are of a high quality and changes may be made to modules where it is necessary and reasonable to do so. Please be aware that minor changes to modules, such as a change to an individual assessment, can occur up until enrolment, although we endeavour to give you as much notice as possible of any changes. Details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/legalgovernancecompliance/governance/actcharterstatutesordinancesandregulations/studenttermsconditions/

Last Updated: 21 November 2024