Programme Specification for the 2025/6 academic year
MSc Security and Data Science
1. Programme Details
| Programme name | MSc Security and Data Science | Programme code | PTS1HPSHPS10 |
|---|---|---|---|
| Study mode(s) | Full Time Part Time |
Academic year | 2025/6 |
| Campus(es) | Streatham (Exeter) |
NQF Level of the Final Award | 7 (Masters) |
2. Description of the Programme
As a prospective student, you'll find that the MSc Security and Data Science program delves deep into the dynamic
world of security, where the integration of expansive data science techniques and Artificial Intelligence (AI) is rapidly
expanding across various security domains. Consider the following examples:
· Law enforcement agencies are employing machine learning algorithms to enhance predictive policing
strategies.
· Extremist groups and hostile actors are exploiting deepfake technology for malicious purposes.
· Machine learning is being leveraged to combat online propaganda and disinformation.
· AI is playing a pivotal role in the development of cutting-edge cybersecurity systems.
· Intelligence agencies are harnessing AI for more effective intelligence gathering.
· Autonomous weapons systems are increasingly reliant on AI for their operations.
In this program, you'll embark on a unique educational journey that combines in-depth knowledge and a profound
understanding of these critical issues in the current threat landscape with hands-on experience in the interdisciplinary
realm of data science. Here, data science involves a fusion of scientific methodologies, statistical techniques, machine
learning, and AI, all aimed at extracting valuable insights from vast, often messy, and unstructured data sources.
By pursuing this program, you'll acquire the specific expertise required to comprehend both the opportunities and
threats posed by these technological advancements within the ever-evolving landscape of security challenges, both
present and future
3. Educational Aims of the Programme
The aim of the MSc Security and Data Science is to provide you with a unique skill set that blends in-depth knowledge of the contemporary security environment and its relationship with emerging technologies, particularly artificial intelligence (AI), with the data science skills that are increasingly in demand from employers in all sectors, not just those in the security, defence, and law enforcement space. The aim of the programme is to therefore produce graduates who are uniquely suited to roles in the security services, specific law enforcement units, various organisations focused on homeland security, national and international policing organisations, and certain civil service departments, as well as being suited to carrying out cutting-edge academic and think-tank research on security-related issues.
The course will be delivered through students taking a number of compulsory and optional modules, which will involve you developing an in-depth understanding of how AI works (i.e. the structure of a neural network and how they convert input into output), the nature of other emerging technologies, and how these technologies are changing the security, defence, and law enforcement landscape. While the exact ordering and content of these sessions will likely change year-to-year as technologies and the security landscape evolve, the session series will be broken down into broadly three sections. The first will provide an understanding of AI, emerging technologies, and some of the macro-level changes seen within the security domain as a result; including a history of technology and security that will enable the class to understand broad patterns of technological advances, adoption, and changes to security and society. The second part will involve weekly “deep dives” into a specific areas of technology and security. These include, but won’t be limited to, the use of generative AI by extremists, changes to the nature of cyberweapons and their role in security, the societal impact of disinformation and the algorithms and social behaviours that underpin it, the use of data and algorithms for policing, the use of AI in drones for search and rescue and military purposes, changes to the nature of intelligence gathering, and how the socio-political consequences of technologies in shaping the nature of conflict; i.e. conflicts over specific resources. The third and final part of the module will involve a summary of the content and an in-depth look at what the current state of technological developments and their relationship with security tells us about possible future challenges.
Alongside this, you will take modules that aim to provide you with data science training. These modules will assume that students have no prior experience of data analysis or computer coding and will be based around the generic programming language Python, which is in widespread use amongst the data science community due to its high-level nature which makes it easy to learn and use. You will first be introduced to the basic principles of coding architecture and the Python syntax before going on to learn how to use Python for data cleaning, manipulation, and analysis. Finally, you will learn several skills and analysis methods that are commonly used in data science and computational social science; i.e. social network analysis, web scraping, and various machine learning algorithms and tools.
You will also engage in an individual dissertation project, which will allow you to use the abovementioned skills and knowledge you have developed to design, implement, and write-up your own research project. This project will include some form of data analysis and/or machine learning/AI implementation. Alternatively, subject to a suitable placement being found, this module provides you the opportunity to do a research project instead of a traditional dissertation collaboration with an organisation.
The remaining credits for the programme will then be made up of optional modules that are run by the University of Exeter and which are detailed below.
4. Programme Structure
5. Programme Modules
The following tables describe the programme and constituent modules. Constituent modules may be updated, deleted or replaced as a consequence of the annual programme review of this programme.
Stage 1: 120 credits of compulsory modules, 60 credits of optional modules
Stage 1
Compulsory Modules
| Code | Module | Credits | Non-condonable? |
|---|---|---|---|
| SPAM002 | Security, Artificial Intelligence and Emerging Technologies | 30 | Yes |
| SPAM003 | Computational Social Science 1 | 15 | Yes |
| SPAM004 | Computational Social Science 2 | 15 | Yes |
| SSIM907 | Policy Analytics: Dissertation or Research Consultancy Project | 60 | No |
Optional Modules
You may take elective modules up to 30 credits outside of the programme in stage 1 of the programme as long as any necessary prerequisites have been satisfied, where the timetable allows and if you have not already taken the module in question or an equivalent module. You may take up to 30 credits of modules at level 6 (i.e. SOC3XXX, SSI3XXX).
| Code | Module | Credits | Non-condonable? |
|---|---|---|---|
| MSc Security and Data Science option modules 2025-6 | |||
| SSIM915 | Statistical Modelling | 15 | No |
| SSIM916 | Machine learning for social data science | 15 | No |
| SSIM918 | Data Visualisation | 15 | No |
| POLM084 | Conflict, Security and Development in World Politics | 30 | No |
| POLM158 | Digital Politics and Policy | 30 | No |
| POLM233 | Applied Strategy in the Contemporary World | 30 | No |
| POLM897 | Surveys and Experiments: Design, Implementation and Analysis | 15 | No |
| SOCM033 | Data Governance and Ethics | 15 | No |
| PHLM019 | AI and Society | 15 | No |
| POLM082 | International Relations of the Middle East | 30 | No |
| POLM156 | The Transformation of Politics in the Global Age | 30 | No |
| POLM217 | Conflict, Security and Development in Eurasia | 30 | No |
| POLM231 | State Crime | 30 | No |
| POLM240 | Security Futures | 30 | No |
| POLM308 | Transnational Security and Terrorism | 30 | No |
| POLM343 | Gender, War and Militarism | 30 | No |
| SOCM019 | Research Methods in the Social Sciences | 15 | No |
| ANTM105 | Humans and Wildlife: Conflict and Conservation | 15 | No |
| ANTM109 | Animal Criminology | 15 | No |
6. Programme Outcomes Linked to Teaching, Learning and Assessment Methods
Intended Learning Outcomes
A: Specialised Subject Skills and Knowledge
| Intended Learning Outcomes (ILOs) On successfully completing this programme you will be able to: | Intended Learning Outcomes (ILOs) will be... | |
|---|---|---|
| ...accommodated and facilitated by the following learning and teaching activities (in/out of class): | ...and evidenced by the following assessment methods: | |
1. Develop an in-depth understanding of the current security-threat landscape and the impact of technological developments upon it, particularly emerging technologies. | 22 hours of scheduled learning, 278 hours of guided independent study, broken down as 64 hours of course readings, 64 hours of research and essay writing, 150 hours of research and practical-based work for final technical report. 44 hours of scheduled learning, 256 hours of guided independent study, broken down as 100 hours of coding practice, 56 hours of coding and analysis for take-home exercise, and 100 hours work for final technical report.
| Point 1: 2 * academic essays. Point 2: 1 * technical report. Point 3: 1* Take-home coding exercise. 1* Project report. |
Intended Learning Outcomes
B: Academic Discipline Core Skills and Knowledge
| Intended Learning Outcomes (ILOs) On successfully completing this programme you will be able to: | Intended Learning Outcomes (ILOs) will be... | |
|---|---|---|
| ...accommodated and facilitated by the following learning and teaching activities (in/out of class): | ...and evidenced by the following assessment methods: | |
4. Understand the security-technology relationship and its transformative impact within the wider socio-political and economic context. | Points 4 & 5: 22 hours of scheduled learning, 278 hours of guided independent study, broken down as 64 hours of course readings, 64 hours of research and essay writing, 150 hours of research and practical-based work for final technical Point 6: 44 hours of scheduled learning, 256 hours of guided independent study, broken down as 100 hours of coding practice, 56 hours of coding and analysis for take-home exercise, and 100 hours work for final technical report. | Points 4 & 5: 2 * academic essay. Point 6: 1* Take-home coding exercise. 1* Project report. |
Intended Learning Outcomes
C: Personal/Transferable/Employment Skills and Knowledge
| Intended Learning Outcomes (ILOs) On successfully completing this programme you will be able to: | Intended Learning Outcomes (ILOs) will be... | |
|---|---|---|
| ...accommodated and facilitated by the following learning and teaching activities (in/out of class): | ...and evidenced by the following assessment methods: | |
6. Develop strong skills in computer programming for a range of tasks, including data analysis, data science, and implementing machine learning/artificial intelligence algorithms; which are transferable to a wide range of different sectors. | Point 7: 44 hours of scheduled learning, 278 hours of guided independent study, broken down as 64 hours of course readings, 64 hours of research and essay writing, 150 hours of research and practical-based work for final technical report. Point 8: 66 hours of scheduled learning, 534 hours of guided independent study, broken down as 64 hours of course readings, 64 hours of research and essay writing, 150 hours of research and practical-based work for final technical, 100 hours of coding practice, 56 hours of coding and analysis for take-home exercise, and 100 hours work for final technical report. | Point 7: 1* problem set. 1* Take-home coding exercise. 1* Project report. Point 8: 2 * academic essays. 1 * technical report.1* problem set. 1* Take-home coding exercise. 1* Project report. |
7. Programme Regulations
Classification
Full details of assessment regulations for all taught programmes can be found in the TQA Manual, specifically in the Credit and Qualifications Framework, and the Assessment, Progression and Awarding: Taught Programmes Handbook. Additional information, including Generic Marking Criteria, can be found in the Learning and Teaching Support Handbook.
8. College Support for Students and Students' Learning
The programme will be delivered by the Centre for Computational Social Science at the University of Exeter, which is based in the Clayden building on the University’s Streatham Campus. The Clayden Building has a computational social science teaching room that consists of 32 high-spec machines that make it perfect for delivering this programme. Additionally, the programme itself will benefit from the large amount of data science/computational social science expertise that is based within the Q-Step Centre.
Students on the programme will all receive a module handbook that will provide all of the necessary details for the programme, including programme structure, nature of assignments, module choices, etc. Additionally, the programme coordinator will provide a single point of contact for these students.
9. University Support for Students and Students' Learning
Please refer to the University Academic Policy and Standards guidelines regarding support for students and students' learning.
10. Admissions Criteria
Undergraduate applicants must satisfy the Undergraduate Admissions Policy of the University of Exeter.
Postgraduate applicants must satisfy the Postgraduate Admissions Policy of the University of Exeter.
Specific requirements required to enrol on this programme are available at the respective Undergraduate or Postgraduate Study Site webpages.
11. Regulation of Assessment and Academic Standards
Each academic programme in the University is subject to an agreed College assessment and marking strategy, underpinned by institution-wide assessment procedures.
The security of assessment and academic standards is further supported through the appointment of External Examiners for each programme. External Examiners have access to draft papers, course work and examination scripts. They are required to attend the Board of Examiners and to provide an annual report. Annual External Examiner reports are monitored at both College and University level. Their responsibilities are described in the University's code of practice. See the University's TQA Manual for details.
14. Awarding Institution
University of Exeter
15. Lead College / Teaching Institution
Faculty of Humanities, Arts and Social Sciences (HASS)
16. Partner College / Institution
Partner College(s)
Not applicable to this programme
Partner Institution
Not applicable to this programme.
17. Programme Accredited / Validated by
0
18. Final Award
MSc Security and Data Science
19. UCAS Code
Not applicable to this programme.
20. NQF Level of Final Award
7 (Masters)
21. Credit
| CATS credits | 180 |
ECTS credits | 90 |
|---|
22. QAA Subject Benchmarking Group
23. Dates
| Origin Date | 16/10/2023 |
Date of last revision | 16/10/2023 |
|---|
