What will I learn on this module?
This module provides a robust foundation in data mining and artificial intelligence (AI), focusing on extracting implicit and valuable insights from data and building intelligent models to utilize these patterns that can be used in commercial, scientific, and other applications. Combining theoretical and practical approaches, it covers key topics such as supervised and unsupervised learning, feature extraction, outlier detection, topic modelling, and handling large datasets. You'll develop the skills to uncover patterns, find anomalies, and predict trends from real-world datasets, while considering the professional, ethical, social, and legal aspects of these technologies.
In addition, the module introduces AI methods and their underlying mathematical, statistical, and natural science theories, including concepts like problem-solving through searching, knowledge representation, reasoning, and machine learning. It prepares you for advanced modules such as Machine Learning, Computing Placement, and the Computing Dissertation, while integrating industrial case studies to demonstrate real-world applications. These include addressing societal and environmental challenges such as equity, diversity, inclusion (EDI), and green computing. Together, these topics equip you with essential skills for solving complex, real-world problems.
In this module, you will walk through a series of typical data mining and AI approaches, making use of Northumbria’s state-of-the-art computer labs. You will also engage with several simplified industry-relevant cases as part of your research-rich learning. The assessment involves a project applying data mining algorithms to uncover patterns and developing AI algorithms to address computing problems in your chosen application domain.
How will I learn on this module?
This module combines lectures, computer-assisted workshops, and independent learning to support your development. Lectures introduce key theories and concepts, including Data Mining and AI algorithms, which you will implement through guided exercises during hands-on workshops in Northumbria’s CIS building computer labs. Practical exercises and real-world examples reinforce your understanding, while independent learning activities, such as exploring directed research papers and reports, encourage deeper engagement with the subject area.
How will I be supported academically on this module?
You will be supported by lecturers during the timetabled sessions when you will receive feedback on your work. The University’s eLearning Portal offers remote access to all lecture and seminar materials to reinforce your learning. In addition, the university library offers support for all students through providing electronic resources.
What will I be expected to read on this module?
All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)
What will I be expected to achieve?
Knowledge & Understanding:
MLO1 – Develop knowledge and understanding of typical data mining and AI algorithms, the underpinning math, statistics, and natural science theories, and their applications
MLO2 – Select and apply data mining and AI algorithms to a directly relevant computing problem and critically evaluate your solution
MLO3 – Demonstrate an understanding of user, professional, ethical, social, legal, and economic issues and risks surrounding the design, development, operation, and maintenance of AI systems
Intellectual / Professional skills & abilities:
MLO4 - Develop computational thinking and its relevance to automation and everyday life
Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
MLO5 – Evaluate data mining and AI applications and their societal and environmental implications
How will I be assessed?
Formative assessment: Exercises provided and carried out within practical classes and workshops will build up to form a basis for the summative assessment. Feedback will be given during these practical classes and workshops and/or through discussions via email/blackboard forum.
Summative Assessment: This assessment involves solving a real-world problem using data mining and AI algorithms, culminating in a 2,500-word report. The report will include a critical literature review of data mining methods and a specific subfield of AI algorithms, emphasizing their societal and environmental implications. You will develop and present a solution, report your findings, and critically evaluate the methods and algorithms used. Additionally, you will discuss the associated user, professional, ethical, social, legal, and economic considerations and risks. This assessment accounts for 100% of the marks, addresses MLO 1-5, and includes written feedback on your submission.
Pre-requisite(s)
No pre-requisite
Co-requisite(s)
No co-requisite
Module abstract
This module equips you with the knowledge and practical skills to apply data mining and artificial intelligence (AI) methods to address real-world problems. Blending theoretical and practical approaches, it covers core principles, underpinning theories, and practical implementations of data mining and AI techniques, such as knowledge and pattern discovery (e.g., anomaly detection, relationship discovery), knowledge representation, reasoning, search (optimization), and machine learning. Topics are introduced through lectures and explored via real-world examples, practical exercises, and hands-on workshops in advanced computer labs, enabling you to apply these methods effectively. You will gain experience in solving realistic business problems while developing independent learning skills to deepen your expertise. Societal, environmental, professional, ethical, social, legal, and economic considerations are integrated throughout the module and its assessments. The module concludes with a substantial project where you will consolidate your skills, supported by formative feedback, and explore the potential for real-world applications and future commercialization.
Course info
UCAS Code G407
Credits 20
Level of Study Undergraduate
Mode of Study 3 years Full Time or 4 years with a placement (sandwich)/study abroad
Department Computer and Information Sciences
Location City Campus, Northumbria University
City Newcastle
Start September 2025 or September 2026
All information is accurate at the time of sharing.
Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.
Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.
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