MSc Data Science

  1. Course Variations
    Nomenclature Duration Mode Of Attendance
    MSc 1Yr FT
    MSc 2Yr PT
  2. Typical offer:
    UK 2:1
  3. Swansea University wins the Postgraduate category!

Course Overview


This programme aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.

Key Features

  • We are top in the UK for career prospects [Guardian University Guide 2018]
  • 5th in the UK overall [Guardian University Guide 2018]
  • 7th  in the UK for student satisfaction with 98% [National Student Survey 2016]
  • We are in the UK Top 10 for teaching quality [Times & Sunday Times University Guide 2017]
  • 12th in the UK overall and Top in Wales [Times & Sunday Times University Guide 2017]
  • 92% in graduate employment or further study six months after leaving University [HESA data 2014/15]
  • UK TOP 20 for Research Excellence [Research Excellence Framework 2014]
  • Our Project Fair allows students to present their work to local industry
  • Strong links with industry
  • £31m Computational Foundry for computer and mathematical sciences will provide the most up-to-date and high quality teaching facilities featuring world-leading experimental set-ups, devices and prototypes to accelerate innovation and ensure students will be ready for exciting and successful careers.  (From September 2018)
  • Top University in Wales [Times & Sunday Times University Guide 2017]


Disclaimer: Module selection options may change.

MSc 1 Year Full-time

Year 1 (Level 7 PGT)

FHEQ 7 Taught Masters / PGDip / PGCert

Students choose 120 credits from the following:

Compulsory Modules
Module CodeSemesterCreditsModule Name
CSCM10Semester 1 and Semester 215Computer Science Project Research Methods
CSCM27Semester 1 (Sep-Jan Taught)15Visual Analytics
CSCM35Semester 1 (Sep-Jan Taught)15Big Data and Data Mining
CSCM45Semester 2 (Jan - Jun Taught)15Big Data and Machine Learning
CSCM70Semester 1 (Sep-Jan Taught)15Mathematical Skills for Data Scientists
Optional Modules

Year 2 (Level 7 PGT)

FHEQ 7 Taught Masters Dissertation
Compulsory Modules
Module CodeSemesterCreditsModule Name
CS-M20Semester 1 and Semester 260MSc Project


Large data sets are now available in almost all modern activities, and the ever growing amount of data requires new and innovative technologies and well equipped data scientists. The demand for data scientists in the UK has grown exponentially in recent years. Despite rapid expansion by the universities in the past few years, it has been predicted by multitude studies that the industry will continue to experience supply shortage of data scientists.

The programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mining both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.

The programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.

Details of current Part Two projects can be found here

Entry Requirements

Minimum 2:1  (or equivalent) in Computer Science or a related discipline.  Candidates with a 2:2 will be considered if they have relevant background.

IELTS 6.0 (with a minimum of 5.5 in each component) or equivalent English test.

International students:  please visit our International pages for information on entry requirements for your country:  Africa, South Asia, East Asia, Europe and Russia, Middle East, North America, South America, South East Asia.  If your country is not listed within these regions, please contact

How To Apply

For all EU/International enquiries please contact:

+44 (0)1792 295142

For UK applicants

For International applicants

Tuition Fees

Annual tuition fees for entry in the academic year 2018/19 are as follows:

UK/EU International
MSc Full-time £7,900 £16,750
MSc Part-time £3,950 £8,400

Tuition fees for years of study after your first year are subject to an increase of 3%.

You can find further information on fees and how to pay on our tuition fees page.

You may be eligible for funding to help support your study. To find out about scholarships, bursaries and other funding opportunities that are available please visit the University's scholarships and bursaries page.

International students and part-time study: If you require a Tier 4 student visa you must be studying full-time. If you are in the UK under a different visa category, it may be possible for you to study part-time. Please see our part-time study and visas page for more information.

Current students: You can find further information of your fee costs on our tuition fees page.

Additional Costs

The tuition fees do not cover the costs of purchasing books or stationery, printing, thesis binding or photocopying costs.

There are no mandatory additional costs specified for this course.


Year 1 (Level 7 PGT)

FHEQ 7 Taught Masters / PGDip / PGCert

Students choose 120 credits from the following:

Compulsory Modules

  • CSCM27 Semester 1 (Sep-Jan Taught) 15 Visual Analytics
  • CSCM30 Semester 2 (Jan - Jun Taught) 15 Data Science Research Methods and Seminars
  • CSCM35 Semester 1 (Sep-Jan Taught) 15 Big Data and Data Mining
  • CSCM45 Semester 2 (Jan - Jun Taught) 15 Big Data and Machine Learning
  • CSCM70 Semester 1 (Sep-Jan Taught) 15 Mathematical Skills for Data Scientists

Optional Modules

Data Science Options

  • Choose  Exactly 45 credits from the following Modules:
  • CSCM37 Semester 1 (Sep-Jan Taught) 15 Data Visualization
  • CSCM39 Semester 2 (Jan - Jun Taught) 15 Human Computer Interaction
  • CSCM58 Semester 1 (Sep-Jan Taught) 15 High Performance Computing in C/C++
  • CSCM67 Semester 2 (Jan - Jun Taught) 15 Graphics Processor Programming
  • CSCM77 Semester 1 (Sep-Jan Taught) 15 Computer Vision and Pattern Recognition
  • CSCM85 Semester 2 (Jan - Jun Taught) 15 Modelling and Verification Techniques
  • CSCM98 Semester 1 (Sep-Jan Taught) 15 Operating Systems and Architectures

Year 2 (Level 7 PGT)

 FHEQ 7 Taught Masters Dissertation

Compulsory Modules

  • CS-M20 Semester 1 and 2 (Sep-Jun Taught) 60 MSc Project

Career Destinations

  • Data Analyst
  • Data mining Developer
  • Machine Learning Developer
  • Visual Analytics Developer
  • Visualisation Developer
  • Visual Computing Software Developer
  • Database Developer
  • Data Science Researcher
  • Computer Vision Developer
  • Medical Computing Developer
  • Informatics Developer
  • Software Engineer


The Department is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of our expansion, we are building the Computational Foundry on our Bay Campus for computer and mathematical sciences. This development is exciting news for Swansea Mathematics who are part of the vibrant and growing community of world-class research leaders drawn from computer and mathematical sciences.