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CSC-44100 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

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

CSC-44100 - Mathematical Techniques for Computational Sciences
Coordinator: Danila Prikazchikov Tel: +44 1782 7 33414
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
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

A knowledge of some of the fundamental concepts in mathematics is important to all our MSc programmes, and something that will give you enhanced understanding in areas such as AI, data management, and cyber security. This module is designed for students without recent mathematical knowledge or experience and will support you to explore the power of linear algebra for solving complex systems, use probability and statistics to make data-driven decisions, and harness the beauty of calculus to understand dynamic changes.

Aims
This module aims to enable students from non-mathematical backgrounds to:
• understand an introduction to the mathematical concepts relevant to areas of Computer Science such as AI, Data Science and Cyber Security
• develop knowledge and skills to analyse and solve real-world AI and data science problems
• understand and apply topics such as algebraic equations, vectors, matrices, systems of linear equations, calculus of functions of one and several variables, probability, and statistics

Intended Learning Outcomes

Evaluate various mathematical approaches to analysing a given data set: 2
Apply suitable techniques to solve algebraic equations: 1,2
Perform operations with vectors and matrices: 1,2
Solve systems of linear equations: 1,2
Select and apply suitable techniques to solve problems in calculus: 1,2
Perform probability calculations, including conditional probability: 1,2
Analyse data using statistical methods: 1,2

Study hours

12 x 2 hours of lectures
12 x 1 hours of practicals/problem classes
Independent study: 84 hours
Report preparation: 30 hours

School Rules

None

Description of Module Assessment

1: Problem Sheets weighted 25%
Set of 5 online tasks
Students will be given 5 weekly short online tasks (equally weighted and marks summed) to complete. The tasks complement the content taught during that week. Sheets can be completed anywhere once released and are available online for a limited period during the appropriate week. This will enable feedback to be given as the course progresses, preparing students for the final report.

2: Report weighted 75%
Report
Report comprising 3 sections that contain explanations of steps taken to solve a set of problems applied to real world data, including the reasons for taking those steps, presentation of the results and a summary of the approach taken. 1. solution of linear algebra problems 2. optimisation of functions of one and multiple variables 3. analysis of data representing a set of inter-related random variables Word equivalent of 3,750 words.

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