PgDip Applied Data and AI
01 Sep 2026 - 31 Aug 2027
| Course Leader | Rebecca Peters |
|---|---|
| Course Team | Penny Holborn, Stephanie Perkins |
| Awarding Body | University of South Wales |
| Teaching Institutions | University of South Wales |
| Modes of Study | Part Time |
Document Version
| Version | 4 |
|---|---|
| Valid From | 01 Sep 2026 |
| Valid To | 31 Aug 2027 |
QAA Benchmarks
Educational Aim
The course aims to develop the knowledge and skills needed for an understanding, critical appraisal and application of the practical techniques pertinent to the Data and AI in industry. In addition, it aims to enhance graduates’ confidence in the analysis and interpretation of real-world data and provide them with advanced analytical, evaluative, problem solving skills to provide solutions to business problems.
Professional and vocational skills (including team working, presentational skills, digital awareness, emotional intelligence, creativity, and vision) will be embedded across all modules. Key skills focused on throughout include communication, enquiry and analysis, problem solving, IT skills, working with others and critical reflection.
The qualification has the following generic aims:
To provide an education in the central themes and techniques of modern applicable computing Data and AI relevant for the Financial Services sector.
To enable graduates to apply their knowledge, understanding and skills, to areas, techniques and applications beyond those they have directly experienced.
To provide graduates 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 appropriate for the workplace, to work on their own initiative and handle varying workloads while maintaining standards and targets.
To develop professional skills, including communication, working with others, problem solving and improving own learning and performance, and apply these to non-trivial problems.
The Postgraduate Diploma has the following specific aims:
To understand the critical role of data in a commercial environment.
To understand the concepts of analytical techniques and explain their use in the wider context of Data and AI.
To demonstrate the ability to utilise and implement a variety of algorithms and data manipulation tasks using industry standard programming languages in a workplace environment.
To demonstrate the knowledge, understanding and ability to implement a variety of analytical models to real world situations using industry software utilised in the field of Data and AI in a workplace environment.
To take responsibility for planning and developing a work-based project in Data and AI.
To develop an understanding and consideration of the ethical & legal considerations in Data & AI and how it impacts the workplace environment.
To apply state of the art predictive modelling techniques to a substantial problem and utilise industry standard packages to model work-based problems.
To be capable of effective and clear communication to explain, with clarity, the application of complex methodologies to a substantial problem.
To exercise a critical knowledge, understanding and ability to implement a wide range of complex predictive models in the field of Data and AI.
To critically reflect on attitudes and behaviours appropriate for the workplace.
To exercise broad autonomy and judgement across a significant area of work or study.
Learning Outcomes
| A1 | To determine and use advanced analytical techniques to assess, critically analyse, interpret, and evaluate complex datasets. |
| A2 | To apply state of the art predictive modelling techniques to a substantial problem and utilise industry standard packages to model work-based problems. |
| A3 | Critically evaluate contemporary advanced technological developments in a wide variety of Data and AI business contexts. |
| A4 | To critically reflect on attitudes and behaviours appropriate for the workplace. |
| B1 | To take responsibility for planning and developing a project, and to exercise broad autonomy and judgement across a significant area of work or study. |
| B2 | To select appropriate data visualisation tools to produce an intelligible data story for non-specialist audiences. |
| B3 | To select appropriate analytical techniques to solve complex problems in the field of Data and AI. |
| B4 | To utilise a wide range of Data and AI skills using industry software to evaluate and draw insights from complex problems in the field of Data and AI. |
| C1 | The ability to understand organisational culture and practices and adapt personal behaviour and attitudes appropriately to fit in with that culture. |
| C2 | To apply advanced programming concepts to a substantial problem and utilise standard packages to model simple well-defined problems. |
| C3 | To be capable of effective and clear communication to explain, with clarity, the application of complex analytical methods to a substantial problem. |
| C4 | To work individually and to produce final written reports on project achievements by agreed deadlines. |
Course Structure
Level 7 Modules
| Module Code | Module Id | Module Title | Module Status | Credit Value | Module Type |
|---|---|---|---|---|---|
| MS4D02 | MOD012464 | Data and AI Project | Running | 40 | specified |
| MS4H01 | MOD012082 | Data Analytics | Running | 10 | core |
| MS4H02 | MOD012083 | Programming for Data Analysis | Running | 10 | core |
| MS4H03 | MOD012084 | Machine Learning | Running | 10 | core |
| MS4H04 | MOD012085 | Time Series and Forecasting | Running | 10 | specified |
| MS4H05 | MOD012086 | Text Mining and Natural Language Processing | Running | 10 | specified |
| MS4H20 | MOD013082 | Advanced Tools and Techniques for Data Science | Running | 10 | specified |
| MS4S12 | MOD012088 | Professional Skills for Data and AI | Running | 20 | specified |
Teaching and Assessment
Learning and Teaching Methods
Employer Engagement
The intensive Bootcamp stage of the programme will include guest speakers from across the employer consortium.
The programme involves continual liaison with the employers in the consortium.
Means of Assessment
Every taught module has at least one hour of tutorial or practical associated with every hour of seminar, where students are able to practise what they are learning. A further weekly support session will allow for individual feedback.
Tutor-led synchronous seminars allow flexible classroom time for students to learn by doing, to practise, to discuss and to demonstrate their work.
Seminars and tutorials contain formative exercises to encourage students to experiment and gain practical experience.
Learning Support
A 4-week intensive bootcamp aims to prepare the graduates for the programme but also to add value to the employer from the first day.
Along with the required taught modules that will be undertaken during the intensive bootcamp, the proposed schedule aims to provide a practical introduction to the programme coupled with professional employment behavioural expectation. The aim is that the bootcamp will manage graduate expectations from the outset and by the end, ensure that graduates are brought up to the same minimum level of understanding across several key areas.
Activities include, a launch event, hackathon and employer led sessions.
The induction will also aim to ensure that all parties are given a full understanding of the course requirements and will explain how the work-based learning and taught aspects of the course operate.
A programme brochure will be issued on the first day (given to graduates and their managers) that will supplement the more traditional student handbook information.
In summary, the information given at induction will include:
Course and module aims and learning outcomes
Week by week timetables and subject content
Assessment outlines and requirements
Assessment timetable
Details of support services available (such as library, IT, study advice)
Academic requirements.
The University’s ICIS system provides further access to course information and module definitions.
Course Exit Points
| Award | Criteria | Final |
|---|---|---|
| Postgraduate Diploma | 120 credits of which at least 90 must be at Level 7 and no more than 30 at Level 6 | Final |
| Postgraduate Certificate | 60 credits with at least 40 at Level 7 and no more than 20 at Level 6 | Intermediate |
Progression Route
Typically, postgraduates continue their careers in industry or commence doctoral research. Graduates would also have the option to progress to the MSc Data Science.
Entry Requirements
Admission to the course is typically through the following qualifications:
Individuals recruited onto the Welsh Data & AI Fast Track Graduate Programme are required to hold a numerate first degree with a classification of 2:1 or above. Applicants need to possess the right to work in the UK and hence the course will not recruit Tier 4 students.
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