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CSC-30041 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2026/27 Last Updated: 16 September 2026

CSC-30041 - Machine Learning Applications
Coordinator: Nadia Kanwal Room: CR038
Lecture Time:
Level: Level 6
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2026/27

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

CSC-10058 Introduction to Data Science I
CSC-10060 Introduction to Data Science II

Barred Combinations

None

Description for 2026/27

Students will develop an understanding of Machine Learning techniques and their application to Data Science. The module will focus on developing the skills of a professional data scientist in the context of designing and evaluating data science workflows for a project utilising machine learning. In addition, students will develop an understanding of the key developments in Data Privacy and Ethical AI and apply their knowledge in the design of their workflow and evaluation of their data science project.

Aims
The module provides in-depth training in the use of machine learning tools and techniques that will be used in order to analyse real world data and to deliver valuable insight that can be used to provide business services.

Intended Learning Outcomes

Apply appropriate machine learning techniques to real-world data sets: 1
Develop a complete data science project workflow that demonstrates understanding of ethical design: 1
Evaluate algorithmic decision-making for bias and explainability using performance metrics and fairness testing: 1

Study hours

20 hours of practical: 10 x 2 hour practical sessions
12 hours of lectures
20 hours coursework preparation
98 hours independent learning

School Rules

None

Description of Module Assessment

1: Assignment weighted 100%
Data Science Project Implementation Report (Video Format)
Students will produce a video presentation documenting the planning, implementation, and evaluation of a data science project. The presentation (7-10 minutes long) should demonstrate the project's objectives, methodology, data analysis process, key findings, and critical evaluation of outcomes. Final Submission Requirements 1. A recorded video presentation (7-10 minutes long) outlining the planning, implementation, and evaluation of the project. 2. A ZIP file containing all project files, including fully documented and appropriately commented source code, datasets (where permitted), and any supporting materials required to reproduce the work.

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: 16 September 2026