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CSC-20085 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

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

CSC-20085 - Artificial Intelligence for Data Scientists
Coordinator: Nadia Kanwal Room: CR038
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

Programming for Data Scientists

Barred Combinations

None

Description for 2025/26

Unlock the power of Artificial Intelligence (AI) with our cutting-edge module designed for aspiring data scientists. Dive into AI, machine learning, deep learning, natural language processing, and computer vision. Learn to implement key algorithms like decision trees, neural networks, and more while mastering evaluation metrics such as precision, recall, and F1 score. Engage in hands-on projects using real-world data, and collaborate with industry professionals to solve practical business problems. This module equips you with the essential skills and tools to drive innovation and tackle complex data challenges in your organization, making you a proficient data scientist ready for the future.

Aims
The module aims to equip you with essential AI concepts and advanced techniques, including machine learning, deep learning, natural language processing, and computer vision. Through hands-on projects, you will develop practical skills to implement AI solutions effectively, preparing you to tackle complex data challenges and drive innovation in your organization

Intended Learning Outcomes

Apply artificial intelligence and machine learning techniques to solve a data science problem.
: 1
Critically evaluate machine learning and artificial intelligence techniques relevant to data science.: 1
Implement and document ethics-by-design methodologies in the coursework report to provide a hypothesis-driven data science solution.: 2
Analyze and debate the role of explainable AI (XAI) methods and techniques in providing Data Science and AI solutions that can be understood by humans.: 2

Study hours

Active learning: 40 hrs
Independent Practical work: 40 hrs
Independent learning: 120
assignment preparation: 40
Project work and report writing: 60

School Rules

None

Description of Module Assessment

1: Assignment weighted 30%
Application of Machine Learning Algorithms


2: Project weighted 70%
Solving a complex business problem


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