Financial Data Science MSc - 2023/4
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
Subsidiary award(s)
| Award | Title |
|---|---|
| PGDip | Financial Data Science |
| PGCert | Financial Data Science |
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time | PCL61003 | 180 credits and 90 ECTS credits |
| Part-time | PCL61004 | 180 credits and 90 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
26/04/2024
Educational aims of the programme
- Equip students with the working knowledge of financial markets and product structures.
- Equip students with the skills to model the rise and the fall of financial asset prices based on the applications of Bayesian logic to the flow of information in financial markets.
- Equip students with the skills to program in Python for data science, data analysis, machine learning, and other data-related applications.
- Equip students with the skills to make predictions of certain events by using machine learning and artificial intelligence.
- Provide students with a detailed understanding of the principles and methods of statistical modelling and methodology.
- Give students practical experience of investigating data using statistical software.
- Give students an overview of machine learning problems related to dynamical systems and equip students with a solid understanding of the mathematical tools required to analyse high-dimensional data sets.
- Provide students with hands-on experience in data science and time series analysis.
- Give students the opportunity to apply state-of-the-art machine learning tools to real-world problems.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| Demonstrate the ability of using Python for scientific computing, for data analysis, machine learning, and data assimilation. | KPT | MSc | |
| Demonstrate the capability to apply data analysis tools and to interpret the results and show a systematic understanding of key aspects of selected topics within data science and statistical learning theory. | KCPT | PGDip, MSc | |
| Demonstrate the capability to implement machine learning algorithms and to use established libraries. | PT | MSc | |
| Demonstrate systematic understanding of mathematical aspects of selected topics within dynamical systems theory, data science, and statistical learning theory. | KC | MSc | |
| Demonstrate the capability to apply data-driven mathematical methods for simulation and measurement of data, and to interpret the results. | KCPT | PGCert, PGDip, MSc | |
| Demonstrate the capability to implement machine learning algorithms in Python and to use established machine learning libraries. | KPT | PGDip, MSc | |
| Demonstrate the ability to use advanced regression analysis, regularisation under the energy norm and L1 regression. | CT | MSc | |
| Demonstrate the ability to model dynamical behaviours of random systems driven by information revelation, by means of the techniques of signal processing. | KCT | PGCert, PGDip, MSc | |
| Demonstrate the ability to comprehend the concept of arbitrage pricing in financial markets and apply this in practice using models. | KCT | PGDip, 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 |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time
This Master's Degree programme is studied full-time over one academic year, consisting of 180 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)
Part-time
This Master's Degree programme is studied part-time over two academic years, consisting of 180 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)
Programme Adjustments (if applicable)
N/A
Modules
Year 1 (full-time) - FHEQ Levels 6 and 7
Module Selection for Year 1 (full-time) - FHEQ Levels 6 and 7
Students must select one optional module in Semester 1 and one optional module in Semester 2. Only one L6 optional module can be selected. The Dissertation takes place over the summer period for all students this is post Semester 2 for October students and post Semester 1 for February students.
Year 1 (part-time) - FHEQ Levels 6 and 7
Module Selection for Year 1 (part-time) - FHEQ Levels 6 and 7
Part-time students must select one optional module in year one and one optional module in year two. Only one L6 optional module can be selected across the programme.
Year 2 (part-time) - FHEQ Levels 6 and 7
Module Selection for Year 2 (part-time) - FHEQ Levels 6 and 7
Part-time students must select one optional module in year one and one optional module in year two. Only one L6 optional module can be selected across 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
Digital Capabilities: The programme has a strong element of enhancing digital capabilities of the students, through the theory and practice of programming, as well as data analytics.
Employability: Because a number of compulsory modules employ programming in Python, a preferred programming language in the industry dealing with large data sets, the employability of the students is naturally strengthened. In parallel to this, the programme will provide students with the basic understanding of how financial markets work, as well as how financial contracts are priced and risk-managed in practice, providing the students with strong employability prospects in financial and related sectors.
Resourcefulness and Resilience: Data science is becoming increasingly more important in many areas of applications (and this includes not only the obvious area of Internet search engines and retailers, but also many other areas ranging from finance, insurance, politics, to even game development or policing), so the ability they gain from the programme to manipulate large data sets and to extract useful information through machine learning and other approaches rooted in various areas of mathematics would for sure promote their resourcefulness and resilience in tackling challenges ahead.
Sustainability: Students will learn a range of techniques and skills in statistics and other skills in modelling random phenomenon that in turn are indispensable for understanding and assessing sustainability and related questions, for, assessment of sustainable projects necessarily requires advanced statistical analysis under uncertainties.
Global and Cultural Intelligence: Through a range of compulsory and optional courses, students will learn a range of general ideas such as information and uncertainty, workings of financial markets, and artificial intelligence -- these will stimulate their global and cultural intelligence.
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 2023/4 academic year.