STUDY
BSc (Hons) Computer Science (Artificial Intelligence)
Institution code: | S82 |
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UCAS code: | I103 |
Start date: | September 2025 |
Duration: | Three years full-time, four and a half to nine years part-time |
Location: | Ipswich |
Typical Offer: | 112 UCAS tariff points (or above) BBC (A-Level) DMM (BTEC), Merit (T Level) |
Institution code: | S82 |
---|---|
UCAS code: | I103 |
Start date: | September 2025 |
Duration: | Three years full-time, four and a half to nine years part-time |
---|---|
Location: | Ipswich |
Typical Offer: | 112 UCAS tariff points (or above) BBC (A-Level) DMM (BTEC), Merit (T Level) |
Overview
This course is provided as a pathway on our BSc (Hons) Computer Science degree. All students begin their studies on the BSc (Hons) Computer Science degree before choosing the Artificial Intelligence pathway towards the end of their first year of study. Students who complete this pathway will receive the specialist BSc (Hons) Computer Science (Artificial Intelligence) award at graduation.
Introduction
It is estimated that we currently produce over 2.5 quintillion bytes of data every single day. This data includes over 94 million photos and videos shared on Instagram, over 306 billion emails and over 5 million Tweets. In the last two years alone, an astonishing 90% of the world’s data has been created.
The Artificial Intelligence pathway on our BSc (Hons) Computer Science degree has been designed to provide you with everything you need to be able to find meaning in this data as a successful data scientist and artificial intelligence expert.
Course highlights
- Access to our state-of-the-art DigiTech Centre for specialist modules.
- Access to resources from some of the largest tech companies including Amazon Web Services, Juniper, Oracle and our new Google Student Club
- An opportunity to start your own artificial intelligence business with the University of Suffolk’s Innovation Centre (IWIC) and gain guidance from business leaders and academics.
In the first year of the course, you will gain a solid foundation in data science, artificial intelligence, programming, networks and computer systems. In the second year, you will have the opportunity to create a substantial software product that incorporates software design, implementation and testing. You will also undertake the courses' core research skills module in preparation for your final year project and dissertation. During the final year of your course, you will complete your project and dissertation. For this, you will be assigned a supervisor who is familiar with your chosen topic. You will also study advanced modules in data science, artificial intelligence, cyber security and distributed systems preparing you for a career as a data scientist.
Throughout the course, you are also encouraged to attend an extensive range of seminars and events provided by guest lecturers with backgrounds in technology, business and academia. These same experts often provide real-world briefs for module assessments allowing you to undertake a project specified by industry to meet an organisation’s real needs.
How will you be taught?
You will be taught by experienced lecturers who use their years of industry and research knowledge to demonstrate best practice, industry standards, and innovative technologies. You will experience a variety of teaching methods including lectures and seminar sessions, totalling at least 12 hours of contact time per week. You will also have access to our virtual learning environment, Brightspace, allowing you 24/7 access to lecture material and activities, both on and off campus.
Students also have access to our computing Slack channel allowing them to collaborate and chat with each other. New computing students joining the University will receive access to our ‘New Student’ Slack channel in the weeks before the course commences. This allows new students to get to know each other and make friends before they arrive on campus.
How will you be assessed?
Throughout the course, the emphasis is placed on students completing hands-on projects that they can later present in their professional portfolio to employers. A variety of assessment methods are used, including individual and group-based practical projects, quizzes, technical reports and presentations. There are opportunities for feedback on your work throughout and you will receive the support you need through your lecturers and our Academic Support and Library Services teams.
Computer Science at the University of Suffolk
Course Modules
Our undergraduate programmes are delivered as 'block and blend', more information can be found on Why Suffolk? You can also watch our Block and Blend video.
Downloadable information regarding all University of Suffolk courses, including Key Facts, Course Aims, Course Structure and Assessment, is available in the Definitive Course Record.
This module covers the principles of computer systems, hardware components, the essence of operating systems, and relevant computing-related mathematics. This module will provide the foundational underpinning to enable students to progress deeper into different computing specialisms, and a grasp of the history of computing, recent developments and its possible future.
This module introduces the concepts of communications and networking. It explores the Open Systems Interconnectivity (OSI) 7-layer reference model and TCP/IP Routing Suite (the 5-layer Internet reference model). TCP/IP is the model which is most commonly deployed in the majority of modern-day networks.
The module introduces the concepts of web design, with a focus on designing responsive websites that are targeted at mobile platforms. Students are introduced to HTML, CSS and JavaScript to provide them with an understanding of what goes into the front-end of modern websites. Using a series of case studies, students will analyse the design and layout of a range of existing sites using a number of common analysis techniques.
This module introduces students to the concepts and practice of computer programming. It is aimed at providing students with an understanding of the fundamentals of computer programming by having them work through a range of tasks focused upon layout, structure and functionality.
This module provides an understanding of why cyber security matters to businesses, to society and to individuals, coupled with knowledge of basic concepts, attack techniques, attacker types, and the core elements of cyber assurance.
This module provides an introduction to the artificial intelligence and data science fields, covering the history of the discipline, and exploring a variety of “classical AI” topics.
This module focuses on all phases of the modern software engineering lifecycle and advanced software engineering topics, including critical software, secure software, formal methods and project management from the practitioner’s perspective. This will be put into practice through the requirements gathering, design, implementation and testing of an extensive project that meets the needs of a particular enterprise.
This module provides essential knowledge and appreciation of the role of relational database systems, including basic principles and practice of design, implementation and development for both system designers and software engineers. It will include practical exercises in Structured Query Language.
Research skills are an essential set of capabilities in the toolkit of a professional software engineer. In this module, students will develop knowledge and understanding of the purpose, processes, methods (surveys, experiments, interviews, case studies, etc.), analysis (qualitative and quantitative), and outputs of research and will be able to apply them. This module also delves into the professional, legal and ethical standards and guidelines that inform and guide best practice in business and computing.
This module focuses on data structures (e.g. linked lists, trees, heaps, hash tables, etc), algorithms (sorting, searching, dynamic programming, greedy, graph, geometric, cryptographic, string matching and compression algorithms, etc), and advanced programming techniques and other language paradigms.
Data science includes many techniques for classification, analysis and prediction. This module focuses on those techniques relating to data mining and statistically driven approaches. These techniques also have the advantage of being “explainable AI”, more so than deep learning approaches, and some are long established techniques of “business intelligence”.
Industry, commerce and research are being transformed by the potential to capture, store, manipulate, analyse and visualise data and information on a massive scale. The advent of Big Data with its variety, velocity and volume disrupted the way we store and manage data. During this module you will learn NoSQL approaches to data modelling, database design and manipulation.
The module provides the opportunity for students to apply and develop some of the knowledge and skills acquired in their degree by engaging in a significant project in a specialist area of computing, typically software or networks. It will enable and require students to utilise practical, intellectual and decision-making skills in novel situations and develop their autonomy and self-direction.
A sufficiency of inexpensive computing power, sufficiently large datasets and a number of key theoretical advances created deep learning techniques which have facilitated a wave of accuracy increases across many computational tasks (computer vision, natural language processing, speech recognition, autonomous driving, etc.), making many applications practical. Deep learning is central to modern artificial intelligence. This module explains the underlying mathematics and techniques and how to use them to achieve similar feats of computational accuracy.
This module provides an opportunity to explore in greater depth several areas of artificial intelligence and data science. This will include an understanding of the domain theory, typical problems faced in the domain and how these might be solved.
This module provides a systematic understanding of distributed operating systems, software services and applications in terms of their architectures, functionality and behaviour. It includes case studies on the “Internet of Things” and cloud computing as well as topics on parallel programming.
The module is intended to provide students with an understanding of development for mobile devices with a focus on the constraints of mobile hardware, including interface and networking. Students will learn to integrate input from hardware sensors and work with networked data and services.
On one hand, this provides insights into the mindset of cyber attackers, a secure understanding of the ethics and legal issues in this area, and knowledge and skills in attack technologies and techniques. On the other hand, this module provides a detailed knowledge and understanding of the techniques and tools available to a security professional, and the practical skills in selecting, evaluating, designing, implementing and deploying defences to protect vulnerable software, networks and systems.
WHY SUFFOLK
2nd in the UK for Career Prospects
WUSCA 20243rd in the UK for spend on academic services
Complete University Guide 20254th in the UK for Teaching Satisfaction
Guardian University Guide 2024Entry Requirements
Career Opportunities
Upon graduation from this degree, students can progress into a range of roles, including:
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Cyber Security Expert
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Web Developer
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Data Scientist
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Artificial Intelligence Expert
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Mobile App Developer
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QA Engineer
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Network Engineer
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Software Developer
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Data Analyst
All graduates will also have the opportunity to start their own business within the University of Suffolk Innovation Centre (IWIC). Here, students will have access to hot desk space, networking and collaborative opportunities.
Facilities and Resources
The majority of teaching on this degree will take place on our main Ipswich Waterfront campus and on the top floor of the Atrium building, which houses four high-end computer laboratories complete with industry-standard software and tools.
Specialist modules in data science, artificial intelligence and cyber security may also take place in our state-of-the-art DigiTech Centre at Adastral Park, which was unveiled by Her Royal Highness the Princess Royal in November 2019 and launched in the summer of 2021. The Cyber Security and Digital Forensics Laboratory at Digitech is an advanced facility equipped with high-specification machines. It provides a controlled environment where students can simulate cyberattacks, conduct forensic investigations, and delve into activities such as malware analysis, penetration testing, and cryptographic analysis without affecting other campus networks. In addition, the lab features top-tier Digital Forensics Equipment, which includes high-spec computers and acquisition kits complete with hardware write blockers for forensic image capture from various digital devices.
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