Keele Badge

CSC-20087 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2025/26 Last Updated: 04 December 2025

CSC-20087 - Advanced Mathematics and Statistics
Coordinator: Peter Wootton Tel: +44 1782 7 33767
Lecture Time: See Timetable...
Level: Level 5
Credits: 30
Study Hours: 300
School Office: 01782 733075

Programme/Approved Electives for 2025/26

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2025/26

Computational implementation of mathematics and statistics is one of the cornerstones of data science. This module equips learners with the knowledge of a variety of standard scientific and industry-relevant mathematical and statistical techniques to enable them to make sense of real-world problems. The learners will understand how to apply numerical techniques and mathematical and statistical modelling and evaluate their applicability for a wide variety of problems.

Aims
The module aims to equip learners with the knowledge of a variety of mathematical tools and statistical techniques that enable them to deal with the analysis of real-world problems and datasets.
We also aim to give learners a broad appreciation of the different computational tools at their disposal, and an introduction to numerical analysis. The learners will be able to choose and apply mathematical and statistical models and techniques appropriate to different types of problems.

Intended Learning Outcomes

create appropriate graphics to illustrate a particular problem and/or solution: 1,2,3,4
select and apply appropriate mathematical, statistical and computational tools to help to interpret, solve and analyse a variety of problems, including real world phenomena: 1,2,3,4
assess the options of storing, managing and manipulating data in the context of business organisations: 4
select and apply an appropriate statistical approach to extract information from a dataset, in the general context of data analysis: 3,4
analyse the bounds and numerical precision of floating point numbers and assess numerical convergence of iterative computation methods: 1

Study hours

36 hours practical sessions during block release
36 hours online lectures
218 hours private study
10 hours completing coursework

School Rules

None

Description of Module Assessment

1: Assignment weighted 30%
Written report


2: Class Test weighted 20%
Statistical techniques class test


3: Assignment weighted 20%
Numerical analysis exercise


4: Assignment weighted 30%
Mathematical modelling exercise


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: 04 December 2025