Data Science MSc - 2028/9
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Level 7
Final award and programme/pathway title
MSc Data Science
MSc Data Science with Professional Postgraduate Year (Placement pathway (24 months))
Subsidiary award(s)
| Award | Title |
|---|---|
| PGDip | Data Science |
| PGCert | Data Science |
| PGDip | Data Science with Professional Postgraduate Year |
| PGCert | Data Science with Professional Postgraduate Year |
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time | PCL61001 | 180 credits and 90 ECTS credits |
| Full-time with Placement | PCL71001 | 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 - Computer Science and Electronic Eng
Programme Leader
THORNE Tom (CS & EE)
Date of production/revision of spec
16/10/2026
Educational aims of the programme
- The key educational aim of the programme is to prepare students for a variety of leading roles in data science. Such roles will involve data-intensive computing and lead to positions as data scientists, data analysts, data engineers and data architects, as well as business analysts and database administrators, with expected progression through to managerial roles involving teams of these. Creation, collection, management and analysis of data is core to a wide range of industry activities, from politics to advertising, health, finance, and numerous others.
- The programme incorporates an optional year in industry in order to foster close engagement with the employers of data scientists. This offers an attractive proposition to employers and provides students who have completed the taught programme, and whose subsequent assessment relates to practical experience, with access to potential employers following graduation. An extension to the dissertation emphasises the importance of clear business understanding of value being derived from research and development.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| The principles and practices of data science | K | PGCert, PGDip, MSc | |
| The principles and applications of data science technologies | K | PGCert, PGDip, MSc | |
| The professional issues involved in the exploitation of data | K | PGCert, PGDip, MSc | |
| The areas of emergent and innovative data science technologies | K | PGCert, PGDip, MSc | |
| The key research issues in data science | K | PGDip, MSc | |
| Understand, articulate, and demonstrate how to achieve the requirements of the users of data science applications | C | PGCert, PGDip, MSc | |
| Research, develop, and evaluate data science methods | C | PGDip, MSc | |
| Specify, design and develop solutions to complex and substantial data science problems | C | PGDip, MSc | |
| The practices and business relevance of data science | P | PGCert, PGDip, MSc | |
| The ability to critically evaluate software systems and tools | P | PGCert, PGDip, MSc | |
| The capability to work as an effective member of a team | P | PGCert, PGDip, MSc | |
| The ability to communicate effectively with specialists and non-specialists to understand their needs | P | PGCert, PGDip, MSc | |
| The ability to apply and justify appropriate ways to analyse data and present information | P | PGDip, MSc | |
| The ability to plan, research, manage and implement a major project | P | MSc | |
| Research and information retrieval skills | T | PGCert, PGDip, MSc | |
| Numeracy in both understanding and presenting cases involving a quantitative dimension | T | PGCert, PGDip, MSc | |
| Self-learning skills | T | PGCert, PGDip, MSc | |
| Succinctly present, to a range of audiences, knowledge relevant to the building, testing and deployment of a system | T | PGCert, PGDip, MSc | |
| Time management and organisational skills | T | PGCert, PGDip, MSc | |
| Effective use of specialist IT facilities | T | PGCert, PGDip, MSc | |
| Continuing professional development | T | PGCert, PGDip, 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)
*some programmes may contain up to 30 credits at FHEQ level 6.
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 1 (full-time) - FHEQ Levels 6 and 7
Module Selection for Year 1 (full-time) - FHEQ Levels 6 and 7
SEPTEMBER START
Semester 1 you will study these THREE compulsory modules:
COMM034 - CLOUD COMPUTING
COMM076 - DATABASE SYSTEMS AND BUSINESS INTELLIGENCE
COMM071 - STATISTICAL DATA SCIENCE
And you will need to choose ONE of the following optional modules:
COM3033 - ENGINEERING INTERNET SCALE SYSTEMS
COMM062 - COMPUTATIONAL INTELLIGENCE
Semester 2 you will study these THREE compulsory modules:
COMM072 - RESEARCH, ETHICS, AND SECURITY IN DATA SCIENCE
COMM074 - BUSINESS ANALYTICS WITH DATA VISUALISATION
COMM075 - MACHINE LEARNING FOR DATA SCIENCE
And you will need to choose ONE of the following optional modules:
COMM061- NATURAL LANGUAGE PROCESSING
EEEM005 - AI AND AI PROGRAMMING
EEEM068 - APPLIED MACHINE LEARNING
Over the summer you will study the following core module:
COMM070 - MSC DATA SCIENCE DISSERTATION
FEBRUARY START
First semester (semester 2 according to the academic calendar) you will study these THREE compulsory modules
COMM072 - RESEARCH, ETHICS, AND SECURITY IN DATA SCIENCE
COMM074 - BUSINESS ANALYTICS WITH DATA VISUALISATION
COMM075 - MACHINE LEARNING FOR DATA SCIENCE
And you will need to choose ONE of the following optional modules:
COMM061- NATURAL LANGUAGE PROCESSING
EEEM005 - AI AND AI PROGRAMMING
EEEM068 - APPLIED MACHINE LEARNING
Over the summer you will study the following core module:
COMM070 - MSC DATA SCIENCE DISSERTATION
Second semester (semester 1 according to the academic calendar) you will study these THREE compulsory modules:
COMM034 - CLOUD COMPUTING
COMM076 - DATABASE SYSTEMS AND BUSINESS INTELLIGENCE
COMM071 - STATISTICAL DATA SCIENCE
And you will need to choose ONE of the following optional modules:
COM3033 - ENGINEERING INTERNET SCALE SYSTEMS
COMM062 - COMPUTATIONAL INTELLIGENCE
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 you will study these THREE compulsory modules:
COMM034 - CLOUD COMPUTING
COMM076 - DATABASE SYSTEMS AND BUSINESS INTELLIGENCE
COMM071 - STATISTICAL DATA SCIENCE
And you will need to choose ONE of the following optional modules:
COM3033 - ENGINEERING INTERNET SCALE SYSTEMS
COMM062 - COMPUTATIONAL INTELLIGENCE
Semester 2 you will study these THREE compulsory modules:
COMM072 - RESEARCH, ETHICS, AND SECURITY IN DATA SCIENCE
COMM074 - BUSINESS ANALYTICS WITH DATA VISUALISATION
COMM075 - MACHINE LEARNING FOR DATA SCIENCE
And you will need to choose ONE of the following optional modules:
COMM061- NATURAL LANGUAGE PROCESSING
EEEM005 - AI AND AI PROGRAMMING
EEEM068 - APPLIED MACHINE LEARNING
Over the summer you will study the following core module:
COMM070 - MSC DATA SCIENCE DISSERTATION
You will also study
ENGM324 - EMPLOYABILITY
Year 2 (full-time with placement - 2 years) - FHEQ Levels 6 and 7
| Module code | Module title | Status | Credits | Semester |
|---|---|---|---|---|
| COMM063 | PROFESSIONAL POSTGRADUATE YEAR (DATA SCIENCE) | Core | 60 | Year-long |
| COMM070 | MSC DATA SCIENCE DISSERTATION | Core | 60 | Cross Year |
Module Selection for Year 2 (full-time with placement - 2 years) - FHEQ Levels 6 and 7
COMM063 - Professional Postgraduate Year (Data Science)
COMM070 - MSC DATA SCIENCE DISSERTATION
Opportunities for placements / work related learning / collaborative activity
| Associate Tutor(s) / Guest Speakers / Visiting Academics | Y | |
| Professional Training Year (PTY) | N | |
| Placement(s) (study or work that are not part of PTY) | Y | Yes |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
Other information
The School/Department of Computer Science and Electronic Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This programme develops knowledge, skills and capabilities in the following areas:
Digital capabilities: Strong technical skills are critical to being a data scientist. Modules such as Business Analytics with Data Visualisation and Machine Learning for Data Science provide experience solving technical problems using industry-standard languages such as Python and R and real-world datasets. The MSc Dissertation allows students to design and develop a technical solution to a problem of their choice.
Employability: The programme provides foundational theory and practical skills for careers across industries such as technology and finance. Students develop industry-ready skills using Python and R and work with real-world problems. The placement option provides a year working in industry to improve employment prospects.
Global and cultural capabilities: Computer Science is a global language, and the tools and languages used can be applied internationally. Students work in groups with peers from different backgrounds, developing skills to collaborate internationally.
Resourcefulness and Resilience: Practical problem-solving teaches students to reason about and solve new unseen problems. Coursework requires students to plan, break down large problems and develop practical solutions. Open-ended practical work encourages students to go beyond the taught material and develop innovative solutions. The MSc Dissertation takes an idea from concept through implementation to a professional report.
Sustainability: Computers are embedded within almost every industry, including energy and agriculture, to enhance sustainability. Through the MSc Dissertation, students can work in areas supporting the UN Sustainability Goals.
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.