MSc Data Science
01 Sep 2025 - 31 Aug 2027
| Course Leader | Ieuan Griffiths |
|---|---|
| Course Team | Samuel Jobbins, Rebecca Peters, Stephanie Perkins |
| Awarding Body | University of South Wales |
| Teaching Institutions | University of South Wales |
| Modes of Study | Full Time, Part Time |
Document Version
| Version | 6 |
|---|---|
| Valid From | 01 Sep 2025 |
| Valid To | 31 Aug 2027 |
QAA Benchmarks
Educational Aim
The MSc Data Science has the following generic aims:
To provide an education in the central themes and techniques of modern applicable computing and mathematics relevant for Data Science.
To enable students to apply their knowledge, understanding and skills, to areas, techniques and applications beyond those they have directly experienced.
To provide students with the analysis and interpretation skills to enable them to infer a wide variety of real world problems.
To develop an attitude of personal enterprise and self-responsibility, to work on their own initiative and handle varying workloads while maintaining standards and targets.
To develop transferable skills, including communication, working with others, problem solving and improving own learning and performance, and apply these to non-trivial problems.
The MSc Data Science has the following specific aims:
To understand the concepts and theory of analytical techniques, and explain their use in the wider context of Data Science.
To demonstrate a detailed knowledge and understanding of a wide range of complex analytical concepts in the field of Data Science.
To understand the concepts of modelling real world situations using industry software utilised within Data Science.
To apply advanced programming concepts to a substantial problem and utilise standard packages to model simple well defined problems.
To be capable of effective and clear communication to explain, with clarity, the application of complex analytical methods to a substantial problem.
To take responsibility for planning and developing a project, and to exercise broad autonomy and judgement across a significant area of work or study.
Learning Outcomes
| A1 | To understand the concepts and theory of analytical techniques, and explain their use in the wider context of Data Science. |
| A2 | To demonstrate a detailed knowledge and understanding of a wide range of complex analytical concepts in the field of Data Science. |
| A3 | To understand the concepts of modelling real world situations using industry software utilised within Data Science |
| A4 | To understand current developments in ICT technology, computer programming and database systems, and the wider context in which they are used |
| A5 | To demonstrate a critical understanding of current and developing data models and database systems. |
| A6 | To demonstrate an understanding of the key concepts and techniques associated with the collection and analysis of complex data sets. |
| A7 | To understand current developments in project management, its techniques and tools. |
| B1 | To select appropriate analytical techniques to solve complex problems in the field of Data Science. |
| B2 | To utilise a wide range of Data Science skills using industry software to evaluate and draw insights from complex problems in the field of Data Science. |
| B3 | To plan and develop courses of action that initiate or underpin substantial ICT developments. |
| B4 | To exercise autonomy and judgement in the selection of optimal solutions to database-related problems. |
| B5 | To appraise and contrast strategies for dealing with Big Data |
| B6 | To take responsibility for planning and developing a project, and to exercise broad autonomy and judgement across a significant area of work or study. |
| B7 | To initiate and lead complex tasks and processes, taking responsibility, where relevant, for the work and roles of others. |
| C1 | To work individually and to produce final written reports on project achievements by agreed deadlines. |
| C2 | To be capable of effective and clear communication to explain, with clarity, the application of complex analytical methods to a substantial problem. |
| C3 | To apply advanced programming concepts to a substantial problem and utilise standard packages to model simple well defined problems. |
| C4 | To be able to apply strategic, practical and conceptual understanding in the broad context of ICT application development where there are many interacting factors. |
| C5 | To determine and use appropriate methodologies and approaches to advanced database system applications. |
| C6 | To determine appropriate storage methods for datasets, as well as how to manage, process and query large datasets in order to extract information that can be used to inform decision making processes to meet business goals. |
| C7 | To determine and use appropriate project management techniques and tools in contexts where there are many interacting factors. |
| C8 | To determine and use appropriate research methods and approaches. |
Course Structure
Level 7 Modules
| Module Code | Module Id | Module Title | Module Status | Credit Value | Module Type |
|---|---|---|---|---|---|
| CS4T702 | MOD012616 | MSc Project | Running | 60 | specified |
| IS4S706 | MOD000995 | Project Management and Research Methodology | Running | 20 | specified |
| IS4S761 | MOD009823 | Principles of Computing | Running | 20 | specified |
| MS4S08 | MOD010296 | Applied Statistics for Data Science | Running | 20 | specified |
| MS4S09 | MOD010299 | Data Mining and Statistical Modelling | Running | 20 | specified |
| MS4S16 | MOD012335 | Applied Machine Learning and Deep Learning | Running | 20 | specified |
| MS4S21 | MOD011333 | Big Data Engineering and Applications | Running | 20 | specified |
| MS4T01 | MOD010297 | MSc Project – Data Science | Running | 60 | specified |
Teaching and Assessment
Learning and Teaching Methods
Employer Engagement
Visiting Speakers
There are a number of opportunities for students on this course to engage with visiting speakers and
potential employers. Further details are provided within the Course Handbook.
Volunteering
The School organises a number of events throughout the year such as pop quizzes, masterclasses and
revision days for local school children. Students on this course are able to assist at such events and engage
in a wide range of outreach activities.
Fieldwork
The MSc Project provides a particularly useful opportunity for students to engage with employers, perhaps by
solving a real-life problem or developing a prototype of a potential new software product.
Fieldtrips
Most modules use case studies, scenarios and examples from industry to illustrate concepts and their
importance. Opportunities for work-related learning activities continue as students engage in and contribute
in a positive manner to the solution of world of work tasks and problems
Means of Assessment
• proposal/project
• Dissertation
• Report
• Poster
• Presentation
• Demonstration*
• Practical Coursework
• Group Assessments
Learning Support
Induction
The School plans and runs a programme of induction activities during the first week of attendance for both new and returning students.
The University’s ICIS system provides access to course information and module definitions.
Personal tutor
The Course Leader acts as personal tutor who is able to meet students on a regular basis. The typically small cohort size means that the Course Leader quickly recognises each student and identifies each one’s engagement and progress. If the cohort size was to significantly increase then a Course Tutor would be appointed to assist the Course Leader.
Office hours
Typically staff are available when not teaching.
Tutorials
Every taught module has a weekly practical session with the Lecturer/Tutor, where students are able to practice what they are learning and receive individual support.
Seminars
Tutor-supported seminars allow flexible classroom time for students to learn by doing, to practice, to discuss and to demonstrate their work.
Formative Assessment
Lectures and tutorials contain formative exercises to encourage students to experiment and gain practical experience.
Progress meetings
Each student will meet their Personal tutor once a term to discuss progress.
Research Supervision
A full-time student would normally meet their project supervisor about once a week during the duration of their project for approximately half an hour. For a part-time student equivalent support will be provided, however this will need to be adapted to fit the timing of their project.
Online Resources
Teaching and coursework assessment materials are made available on-line through the University’s virtual learning environment (Unilearn).
Modern computing laboratories provide access to specialist resources. The University also has centrally-managed open-access laboratories for more general work. Each student has an academic e-mail account that is particularly useful when requesting support from teaching and tutorial staff.
Advice Centre's
The University operates an Advice Zone located in the Library.
DDS Service
The University runs a DDS Service that can agree an Individual Support Plan. The Plan summarises the support that has been agreed.
IT/Library
The University has a modern Library that provides access to textbooks, journals, on-line materials and equipment. There are open-access computer laboratories in the Library.
Course Exit Points
| Award | Criteria | Final |
|---|---|---|
| Master of Science | 180 credits of which at least 150 must be at Level 7 and no more than 30 at Level 6 | Final |
| Postgraduate Diploma | 120 credits of which at least 90 must be at Level 7 and no more than 30 at Level 6 | Intermediate |
| Postgraduate Certificate | 60 credits with at least 40 at Level 7 and no more than 20 at Level 6 | Intermediate |
Progression Route
Typically MSc graduates continue their careers in industry or commence doctoral research.
Entry Requirements
Admission to the course is typically through the following qualifications:
The School of Computing and Mathematics seeks actively to promote University policies on equal opportunities and widening access and will seek to recruit as wide a range of students as the current mode of attendance and admission requirements permit.
The procedures, criteria and regulations for admission, including promotion of wider access and equal opportunities will follow those established for the existing post-graduate provision offered by the School of Computing and Mathematics. Normally, evidence will be sought of successful completion of an under-graduate Honours degree and, where appropriate, a minimum average IELTS (International English Language Testing System) score of 6.5.
This course is designed for graduates with a minimum 2:2 Honours degree or equivalent. Entrants should normally be graduates in a numerate discipline.
Candidates applying to the course with non-standard qualifications will be judged on an individual basis using Recognition of Prior Learning procedures as defined in the University’s Regulations. For example, the University may admit students on the basis of their prior experiential learning, provided that it is identifiable, relevant to the programme of study for which they are applying and provides sufficient evidence of their ability.
Students entering the course with 60/120 credits of relevant level 7 material, will be considered for top-up to MSc.
Inclusive Curriculum Statement
The University of South Wales operates a policy of inclusive learning, teaching and assessment to ensure that all students have an equal opportunity to fulfil their educational potential. Course teams will have considered ways of designing out any potentially disadvantageous element of courses during the course design process. However some specific needs may remain, details about how to apply to have your needs assessed can be found at: http://unilife.southwales.ac.uk/pages/3040-disability-and-dyslexia-service/
Addendum for Delivery at a Partner Institution
N/A
Methods Of Quality Standards
N/A
Quality Of Standards Indicators
N/A