Financial Data Science with Industrial Practice MSc - 2028/9
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Levels 6 and 7
Final award and programme/pathway title
MSc Financial Data Science with Industrial Practice (Placement pathway (24 months))
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time with Placement | PCL61010 | 240 credits and 120 ECTS credits |
QAA Subject benchmark statement (if applicable)
Other internal and / or external reference points
N/A
Faculty and Department / School
Faculty of Engineering and Physical Sciences - Mathematics & Physics
Programme Leader
BRODY Dorje (Maths & Phys)
Date of production/revision of spec
15/10/2026
Educational aims of the programme
- To equip graduates, including those from non-mathematical and non-computing backgrounds, with the core knowledge and practical skills in data science, including programming, statistical analysis, data management, and machine learning, to prepare them for a range of careers in data science, finance, analytics and AI.
- To develop students' ability to critically evaluate, select, and apply appropriate tools, technologies, and methods for data analysis and interpretation from diverse data sources, including structured, unstructured, and real-time data and alongside scientific literature and domain knowledge.
- To create an intellectually stimulating and practically relevant learning environment, where students can develop a broad, systematic, and critical understanding of data science theory and applications, and apply this knowledge to address complex, real-world problems across multiple domains.
- To provide access to comprehensive digital resources, professional development opportunities, and academic support, enabling students to develop key digital capabilities, employability skills, and a range of transferable competencies including critical thinking, collaboration and teamwork, and communication with diverse stakeholders.
- To stimulate the development of advanced technical, investigative, and research skills while embedding considerations of ethics, fairness, bias, privacy, security, and legal frameworks throughout the project and data lifecycle.
- To foster globally competent, culturally aware, and resilient graduates, who can work effectively in multidisciplinary, cross-cultural, and rapidly evolving professional environments, while recognising the societal impact of data-driven technologies and promoting sustainable and ethical practices.
- To encourage innovation and curiosity in exploring emerging trends in finance, data science and AI, preparing students to adapt to a rapidly evolving technological landscape.
- To equid students with a working knowledge financial markets and structures including the skills to model the rise and fall of financial asset prices.
- To provide students with the opportunity to obtain substantial industry experience through a year-long placement enabling students to apply theoretical knowledge in financial data science to real-world challenges, develop professional competencies and gain insight into industrial research.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| Apply a comprehensive knowledge of mathematics, statistics, programming, and research methods to solve complex problems in financial data science. | KC | MSc | |
| Formulate and analyse complex data science problems, identifying patterns, trends, and relationships within structured, unstructured and real time datasets to reach substantiated conclusions. | KC | MSc | |
| Select and apply appropriate computational, statistical, and machine learning techniques to model and solve real-world financial data science problems, discussing the limitations of the techniques employed. | KCT | MSc | |
| Demonstrate the ability to comprehend the concept of arbitrage pricing in financial markets and apply this in practice using models. | KCT | MSc | |
| Critically evaluate technical literature, academic research, and industry reports to inform data science methodologies and decision-making processes. | CT | MSc | |
| Demonstrate the ability to price basic financial derivatives such as an option or a credit default swap, within the framework of the information-based asset pricing theory. | KCPT | MSc | |
| Evaluate the societal, ethical, and environmental impact of data science solutions, ensuring responsible and secure use of data and AI technologies. | KCT | MSc | |
| Communicate effectively complex data science concepts, results, and recommendations to both technical and non-technical audiences using data storytelling, visualization, and reporting techniques. | PT | MSc | |
| Demonstrate proficiency in using industry-standard programming languages, data analysis tools to extract, transform, analyse, and visualize data. | KCP | MSc | |
| Develop innovative solutions that address real-world challenges across various domains, including finance, business, healthcare, and technology, while considering societal and industry needs. | KCP | MSc | |
| Enhance employability skills through independent learning, team-work, and research-driven dissertation projects that showcase expertise in specialized areas of data science. | KCT | MSc | |
| Demonstrate the ability to adapt academic knowledge to meet real-world industrial requirements and constraints within a professional working environment. | KCPT | MSc |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time with Placement
This Master's Degree programme is studied full-time over two academic years, consisting of 240 credits at FHEQ level 7*. All modules are semester based and worth 15 credits with the exception of project, practice based and dissertation modules.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
*some programmes may contain up to 30 credits at FHEQ level 6.
Programme Adjustments (if applicable)
N/A
Modules
Year 2 (full-time with placement - 2 years) - FHEQ Levels 6 and 7
| Module code | Module title | Status | Credits | Semester |
|---|---|---|---|---|
| COMM079 | INDUSTRIAL PRACTICE | Compulsory | 60 | Year-long |
Year 1 (full-time with placement - 2 years) - FHEQ Levels 6 and 7
Module Selection for Year 1 (full-time with placement - 2 years) - FHEQ Levels 6 and 7
Semester 1. Note on 'Essential Mathematics for Data Science' and 'Programming for Data Science'. Students with
significant mathematical/ computing backgrounds (as determined in discussion with the programme leader) will take one or two of the modules COMM071, ECO3062 and MAT3052.
Semester 2. Students will choose 2 optional modules from a choice of 3.
Only two L6 optional module can be selected over the duration of the programme.
Opportunities for placements / work related learning / collaborative activity
| Associate Tutor(s) / Guest Speakers / Visiting Academics | N | |
| Professional Training Year (PTY) | N | |
| Placement(s) (study or work that are not part of PTY) | N | |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
Other information
The University of Surrey is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This programme is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Digital capabilities: Hands-on experience with data science tools develops proficiency in industry-standard programming languages, including Python and R.
Employability: The curriculum combines data science, AI and machine learning with financial theory, practical applications and critical reflection. The MSc dissertation enables students to demonstrate their ability to solve complex, high-impact financial data science problems.
Global and cultural capabilities: A diverse, inclusive and collaborative learning environment supports cross-cultural discussion, projects and networking. Students explore data science challenges from multiple perspectives.
Resourcefulness and resilience: Mathematical problem-solving and work with large datasets develop resourcefulness in a rapidly evolving field. The project builds skills in managing complex research tasks, independent inquiry and problem-solving.
Sustainability: Students explore how data science addresses global financial challenges. Professionalism, Ethics and Security for Data Science develops critical understanding of ethical and societal implications, including data privacy, algorithmic bias and responsible AI.
Quality assurance
The Regulations and Codes of Practice for taught programmes can be found at:
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2028/9 academic year.