Programme Specifications

Programme summary

1. Programme Aims

• To inspire students to have interest and enthusiasm for artificial intelligence (AI), and to involve them in an intellectually stimulating and satisfying experience of learning and studying. 
• To give students the knowledge and skills needed to become effective AI specialist professionals within the computing industries. 
• To provide students with specialised areas of study so that they can understand and apply the theory and practice of AI. 
• To provide students with the skills to undertake research projects in AI. 
• To develop, through a variety of educational activities, a range of transferable skills applicable to employment.

2. Relevant subject benchmark statements and other external and internal reference points used to inform programme outcomes:

• QAA Computing Benchmark
• The National Framework for Higher Education Qualifications

3. Programme Learning Outcomes

3.1 Knowledge and Understanding

On successful completion of this programme, students should be able to demonstrate comprehensive knowledge and understanding of: 

K1 Theory and programming methods for the construction of AI systems

K2 AI methods and algorithms

K3 Machine learning and its application to different problems

K4 Data mining and handling large data sets

K5 Image processing and coding

K6 Robotics, control and intelligent/autonomous systems

3.2 Skills and other attributes

a. Subject-specific cognitive skills:

On successful completion of this programme, students should be able to:

C1 Analyse requirements for, model and design computer-based systems in AI

C2 Critically evaluate different machine learning approaches; visual systems and associated technology; and different algorithms in data mining

C3 Analyse commercial and scientific risk associated with a computing project in their specialist area

b. Subject-specific practical skills:

On successful completion of this programme, students should be able to:

P1 Build, implement and test complex computer-based AI systems that are well structured, reliable and useable and take into consideration context and appropriate visual interfaces

P2 Develop practical machine learning models for solving real-world problems

P3 Deploy tools and computer equipment for the implementation and documentation of computer-based AI systems

P4 Implement learning and control algorithms for robotics applications using advanced AI techniques

P5 Apply research methodologies in a computing context to produce novel and leading-edge outcomes

P6 Design a control system that applies to robotics and evaluate its risks and performance

P7 Plan and manage a research project in computer-based systems

c. Key transferable skills:

On successful completion of this programme, students should be able to:

T1 Employ research and information-retrieval skills

T2 Apply calculations to solve cases involving a quantitative dimension

T3 Manage their own learning and development, including time management and organisational skills

T4 Plan and manage a project to complete within schedule and resource availability

T5 Present their work in the form of reports, presentations or demonstrations

4. Programme structure

Semester 1

Compulsory modules (60 credits) 

Semester 2

Compulsory modules (60 credits) 

Semester 3

Compulsory module (60 credits) 

5. Criteria for Progression and Degree Award

In order to be eligible for the award, candidates must satisfy the requirements of Regulation XXI.

6. Relative Weighting of Parts of the Programme for the purposes of Final Degree Classification

N/A

Programme summary

1. Programme Aims

• To inspire students to have interest and enthusiasm for subjects they have chosen to study, and to involve them in an intellectually stimulating and satisfying experience of learning and studying. 
• To give students the knowledge and skills needed to become effective specialist professionals within the computing industries 
• To provide students with specialised areas of study so that they can understand and apply the theory and practice of a range of advanced computing topics 
• To provide students with specialised areas of study so that they can understand and apply the theory and practice of internet computing and network security 
• To provide students with an awareness of the impact and risks of professional, legal, ethical and social issues in their specialist area 
• To provide students with the training to undertake research projects in their specialist area of knowledge in computing 
• To provide training, through a range of educational activities, to develop a range of transferable skills applicable to employment

2. Relevant subject benchmark statements and other external and internal reference points used to inform programme outcomes:

QAA Computing Benchmark
The National Framework for Higher Education Qualifications

3. Programme Learning Outcomes

3.1 Knowledge and Understanding

On successful completion of this programme, students should be able to demonstrate comprehensive knowledge and understanding of: 

K1 Theory and programming methods for the construction of complex systems

K2 Advanced principles of computer networks and distributed computing

K3 Security issues and vulnerabilities for computing and networked systems

K4 The principles of encryption and their application to security problems

K5 Principles of image processing and coding

K6 Principles of robotics, control and intelligent/autonomous systems

3.2 Skills and other attributes

a. Subject-specific cognitive skills:

On successful completion of this programme, students should gain the ability to: 

C1 Analyse requirements for, plan, model and design computer based systems in the specialist fields of the programme

C2 Critically evaluate computer-based systems

C3 Critically evaluate visual systems and associated technology

C4 Critically evaluate networked and Internet-based systems with particular reference to the security of the systems

C5 Plan and manage a research project in computer-based systems

C6 Assess the possible professional, legal, ethical and social issues associated with a computing project in their specialist area

C7 Identify and manage commercial and scientific risk associated with a computing project in their specialist area

b. Subject-specific practical skills:

On successful completion of this programme, students should gain the ability to: 

P1 Build, implement and test complex computer-based systems that are well structured, reliable and useable

P2 Build, implement and test computer-based systems which take into consideration context and appropriate visual interfaces

P3 Build, implement and test internet and networking systems that are secure, reliable and useable.

P4 Assemble and effectively measure and utilise networking equipment

P5 Effectively deploy tools and computer equipment for the implementation and documentation of computer-based systems

P6 Deploy research methodologies in a computing context to produce novel and leading edge outcomes

c. Key transferable skills:

On successful completion of this programme, students should gain the ability to: 

T1 Effectively employ research and information-retrieval skills

T2 Employ numeracy in understanding cases involving a quantitative dimension

T3 Manage their own learning and development, including time management and organisational skills

T4 Work effectively on their own or as part of a team

T5 Plan and manage a project to complete within schedule and resource availability

T6 Present their work in the form of reports, oral presentations or demonstration

4. Programme structure

Semester 1

Compulsory modules  ( 60 credits) 

Semester 2

Compulsory modules  (60 credits) 

Semester 3

Compulsory modules  (60 credits) 

5. Criteria for Progression and Degree Award

In order to be eligible for the award, candidates must satisfy the requirements of Regulation XXI.

6. Relative Weighting of Parts of the Programme for the purposes of Final Degree Classification

N/A

Programme summary

1. Programme Aims

The aim of this programme is to prepare students for data-related professional roles, such as data scientist, data manager and data steward. It will inspire and equip students with the knowledge and skills in entering the data science landscape.

The programme will provide students with:

• A breadth and depth of knowledge and skills in computational, machine learning, and statistical methods needed for real-world data analysis and modelling.
• Knowledge and understanding of fundamental data science, but also on design thinking for AI (Artificial Intelligence) services, data-driven strategy, data governance and ethics, applied story telling for decision making, with optionality to allow students to choose a pathway specific to their prior experience and future aspirations.
• The skillset and demands required by organisations as the programme has been designed in partnership with businesses and industry partners.
• For students who are new to data science, the programme will provide a foundation for a career in data-related professional roles.
• For students who are already working in the sector, the programme will provide opportunities to strengthen and update their knowledge and skills in the areas of data science, data modelling and business intelligence.

2. Relevant subject benchmark statements and other external and internal reference points used to inform programme outcomes:

• QAA Computing Benchmark
• The National Framework for Higher Education Qualifications

3. Programme Learning Outcomes

3.1 Knowledge and Understanding

On successful completion of this programme, students should be able to demonstrate knowledge and understanding of: 

K1 Data types, data governance and ethics, data tolls, techniques and applications

K2 Programming methods for data handling and visualization

K3 Statistics, data mining and their applications

K4 AI and machine learning methods and algorithms

K5 Data modelling, data driven strategies and business intelligence

3.2 Skills and other attributes

a. Subject-specific cognitive skills:

On successful completion of this programme, students should be able to:

C1 Appraise statistical methods for analysing data

C2 Critically evaluate different algorithms for data mining, data visualisation, machine learning and AI

C3 Analyse algorithms designed to carry out specified tasks and convert them into executable programs for data science

C4 Critically evaluate requirements for data governance, ethics and business intelligence

C5 Analyse scientific and commercial risk associated with a data science project

b. Subject-specific practical skills:

On successful completion of this programme, students should be able to: 

P1 Convert a problem into a program to be solved using appropriate programming languages

P2 Develop practical machine learning and AI models for solving real-world problems

P3 Build, implement and test complex data-driven strategies

P4 Apply research methodologies in a computing or business context to produce novel and leading-edge outcomes

P5 Plan and manage a research project in computing or data driven business systems

c. Key transferable skills:

On successful completion of this programme, students should be able to:

T1 Employ research and information-retrieval skills

T2 Apply calculations to solve problems involving a quantitative dimension

T3 Manage their own learning and development, including time management and organisational skills

T4 Plan and manage a project to completion within schedule and resource availability

T5 Present work in the form of reports, presentations or demonstrations

4. Programme structure

4.1 Semester 1 

Compulsory modules – all pathways ( 45 credits) 

Optional modules

Students should select one 15 credit module, guided by prior experience and future employment aspirations.  Modules chosen in discussion with Module Leader/Programme Director.

4.2 Semester 2

Compulsory modules – all pathways (15 credits) 

Optional modules

Students should select 45 credits from the below table, guided by prior experience and future employment aspirations.  Modules chosen in discussion with Module Leader/Programme Director.

4.3 Semester 3 

Compulsory modules (60 credits) 

4.4  Model pathways to assist in option choice

Below are example pathways that students may wish to take based on their qualifications and experience. Other combinations are also acceptable and students are encouraged to discuss their preferences, experience and career aspirations with the Module Leaders/Programme Director to inform their choice.

a)    Model Pathway 1: Typically students with a background in STEM subjects with previous programming experience who wish to gain advanced computing skills would choose to study the following core modules, and optional modules that are biased towards advanced computing techniques:

Semester 1: Introduction to Data Science, Programming for Data Science, Data Governance & Ethics and Statistical Methods & Data Analysis

Semester 2: Research Methods, Applied Machine Learning, Data Mining, and Artificial Intelligence and Big Data.

Semester 3: Data Science Project (example topics could include sentiment extraction and analysis from tweets; text analysis for fake news detection; bank customer churn prediction; predicting house prices with machine learning; predicting skin cancer from medical data and predicting crime using time and location data)

b)    Model Pathway 2: Typically students with a background in Non-STEM subjects and without programming experience who wish to have a career expansion towards Data-Related Professional Roles would choose to study the following core modules, and optional modules that are biased towards core Data Science skills:

Semester 1: Introduction to Data Science, Programming for Data Science, Data Governance & Ethics and Building Data Driven Strategy

Semester 2: Research Methods, Design Thinking for AI-driven services, Stories as Data: Storytelling Approaches for Decision Making, and Artificial Intelligence and Big Data.

Semester 3: Data Science Project (example topics could include sentiment extraction and analysis from tweets; text analysis for fake news detection; bank customer churn prediction; predicting house prices with machine learning; predicting skin cancer from medical data and predicting crime using time and location data)

5. Criteria for Progression and Degree Award

In order to be eligible for the award, candidates must satisfy the requirements of Regulation XXI.

6. Relative Weighting of Parts of the Programme for the purposes of Final Degree Classification

N/A