Programme Specifications

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 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