Mathematical Data Science MSc - 2022/3

Awarding body

University of Surrey

Teaching institute

University of Surrey


FHEQ Levels 6 and 7

Final award and programme/pathway title

MSc Mathematical Data Science

Subsidiary award(s)

Award Title
PGDip Mathematical Data Science
PGCert Mathematical Data Science

Modes of study

Route code Credits and ECTS Credits
Full-time PGB61008 180 credits and 90 ECTS credits

QAA Subject benchmark statement (if applicable)

Other internal and / or external reference points


Faculty and Department / School

Faculty of Engineering and Physical Sciences - Mathematics

Programme Leader

BRODY Dorje (Maths & Phys)

Date of production/revision of spec


Educational aims of the programme

  • Equip students with the skills to program in Python (industry-preferred programming language) for data science, data analysis, machine learning, and other data-related applications. This will enhance their analytic resourcefulness.
  • Equip students with the skills to model and make predictions of certain events by using machine learning and artificial intelligence. Such skills are in high demand, thus enhance their employability.
  • Provide students with a detailed understanding of the principles and methods of statistical modelling and methodology, ranging from the basics to the advanced. These concepts not only enhance their resourcefulness but also are important ingredients in understanding sustainability.
  • Give students practical experience of investigating data using statistical software. This will strengthen their digital capabilities.
  • 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. Potential applications of such an understanding are wide ranging, and will enhance student¿s cultural intelligence.
  • Provide students with hands-on experience in data science and time series analysis. This will strengthen their digital capabilities and strengthening their employability.
  • Give students the opportunity to apply state-of-the-art machine learning tools to real-world problems. This will naturally enhance their resourcefulness and resilience, providing them with skills to tackle new challenges.

Programme learning outcomes

Attributes Developed Awards Ref.
Apply Python for scientific computing, data analysis, machine learning, and data assimilation. KPT PGCert, PGDip, MSc
Demonstrate the ability to apply data analysis tools on a given data set and to interpret the results. KCPT MSc
Demonstrate a systematic understanding of selected topics within data science, statistical, and machine learning theory. KCPT PGCert, PGDip, MSc
Demonstrate the capability to implement machine learning algorithms and to use established libraries. PT MSc
Demonstrate systematic understanding of key aspects of selected topics within modern statistics. KC PGDip, MSc
Apply key aspects of selected topics in statistics in well-defined contexts, showing judgement in the selection and application of tools and techniques. KCP MSc
Demonstrate systematic understanding of mathematical aspects of selected topics within dynamical systems theory. KC PGCert, PGDip, MSc
Demonstrate systematic understanding of mathematics underpinning data science and statistical learning theory. KC PGDip, 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 Matlab/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 understanding of mathematically modelling and analysing selected real-world phenomena. KCT PGCert, PGDip, MSc

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Programme structure


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)
*some programmes may contain up to 30 credits at FHEQ level 6.

Programme Adjustments (if applicable)



Year 1 (full-time) - FHEQ Levels 6 and 7

Module Selection for Year 1 (full-time) - FHEQ Levels 6 and 7

The students must take four optional modules (of which maximum of two modules can be taken from the level 6 modules). Note that the pre-requisites specified in Level 6 modules are applicable only for UG programmes.
Students obtaining 60 credits (a minimum of 45 credits at FHEQ level 7 with remainder at FHEQ level 6) are eligible for Postgraduate Certificate in Data Science ; those obtaining 120 credits (a minimum of 90 credits at FHEQ level 7 with the remainder at FHEQ level 6) are eligible for Postgraduate Diploma in Mathematical Data Science; those obtaining a minimum of 150 credits at FHEQ level 7 ( with 60 credits for the Research Project) with the remainder at FHEQ level 6 are eligible for a Master of Science in Mathematical Science.

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 programme has a strong element of enhancing digital capabilities of the students, through the theory and practice of programming. Because a number of 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. Further, 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. Additionally, students will learn a range of techniques and skills in statistics, such as Bayesian inference, regression techniques, or stochastic filtering theory, that in turn are indispensable for understanding and assessing sustainability. This follows because assessment of sustainable social projects necessarily requires advanced statistical analysis; to study the long-term impact of a project, for instance, entailing a range of uncertainties, the only meaningful way of obtaining quantitative analysis relies on statistical investigations. In addition to these, a range of optional courses such as finance, artificial intelligence, or biology, will further 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 2022/3 academic year.