The University of Southampton

MSc Data Science (1 year full-time)

This one year MSc Data Science degree prepares you to become a proficient data scientist, building core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques.

Due to high demand, this course is now closed to international applicants. This course remains open for UK and EU applicants. Any non-UK/EU applications received after this time will not be processed. We have a range of other related postgraduate courses that you may want to consider.

Not sure if you classify as an International student? Check our fee status page.

Southampton is a University Partner of The Alan Turing Institute, the UK’s national institute for data science and artificial intelligence

Introducing your degree

This MSc programme will train students to become proficient data scientists.

You will gain advanced knowledge in areas such as data mining, machine learning, and data visualization, including state of the art techniques, programming toolkit, and industrial and societal application scenarios.


This programme prepares you to become a proficient data scientist, developing your specialist knowledge in subjects that are crucial for mastering the vast and ever-so-complex information landscape that is characteristic to modern, digitally empowered organisations.

This is typically linked to a number of core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to the know-how that is required to devise and apply sophisticated Big Data analytics techniques, and the creativity involved in designing powerful visualizations.

In the first semester you start with a review of key topics in data science. The course will introduce the core theoretical and technology components required to design and use a data science application, using open-source tools and openly accessible data sets. You will also cover the most important machine learning techniques, which are at the core of any attempt to analyse and reason about data.

You will be exposed to more advanced topics in data mining in the second semester, including feature engineering, methods to manipulate text and multimedia data, topic modelling, social network analysis, and spectral analysis. A new module on data visualization will introduce the most common types of visualization techniques and state-of-the-art technology used to build graphic elements into data science applications to present analytics results.

Finally, during the summer the MSc project enables you will demonstrate your mastery of specialist techniques, relevant methods of enquiry, and your ability to design and deliver advanced application, systems and solutions to a tight deadline, including the production of a substantial dissertation.

View the 2020/21 programme specification document for this course

Programme Structure

Twelve months, full-time.

The programme has been designed to maximise student choice by allowing you to tailor the structure to suit your own strengths and interests. You can choose areas that reflect your personal interests and work on an individual project. You will also take a number of compulsory modules to ensure you are exposed to key topics in all areas. The modules cover state-of-the-art techniques, technologies, and supporting tools, and will expose you to their applications in meeting emerging business needs and ambitious societal problems. Application areas will include: data journalism, Open Government Data, finances, and social media. 

To Apply

You can apply for the programme through the University of Southampton's online postgraduate application system. Visit our how to apply pages for more information. Please note we are part of the Faculty of Physical Sciences and Engineering (FPSE).

The deadline for new applications to this course is 31 July each year.

Key Facts

Southampton University has pioneered many of the most important advances in computer science and web technology of the past 10 years

We are in the top 10% in the UK for the volume and quality of our Computer Science research (REF 2014)

100% of our Computer Science research impact is world-leading or internationally excellent (REF 2014)

Southampton is ranked in the top 100 universities for Computer Science in the 2018 QS World Rankings, and top 10 in the UK

We are in the UK top ten for Computer Sciences (Guardian University Guide, 2019)

Southampton is a University Partner of The Alan Turing Institute, the UK’s national institute for data science and artificial intelligence

We are recognised as an Academic Centre of Excellence in Cyber Security Research by the UK Government and our academics have played a leading role in establishing a European Data Science Academy

Entry Requirements

Typical entry requirements

Honours Degree:

A UK bachelor’s degree with a minimum (2:1) degree (or equivalent) in Computer Science, Computer Engineering, Software Engineering, Mathematics/Statistics, and a high 2:1 in required modules*.  

See international equivalent entry requirements. This is a list of the international qualifications that are recognised by the University of Southampton. If you are not sure that your qualifications meet the requirements of this course please contact our Admissions Teams.

*The required modules are: 

  • At least one programming module (MATLAB, R, Python or other statistics package)
  • At least one advanced maths module, ideally statistics and probability

English Language Requirements:

All applicants must demonstrate they possess at least a minimum standard of English language proficiency: Band C, IELTS 6.5 overall, with a minimum of 6.0 in all components. Find out more about the University’s English Language requirements

International Qualifications:

We welcome applications from international students. For information on applying, visit the International Office website

Selection process:

All individuals are selected and treated on their relative merits and abilities in line with the University's Equal Opportunities Policy. Disabled applicants will be treated according to the same procedures as any other applicant with the added involvement of the Disability Office to assess their needs. The programme may require adaptation for students with disabilities (eg hearing impairment, visual impairment, mobility difficulties, dyslexia), particularly the practical laboratory sessions, and we will attempt to accommodate students wherever possible.


The first semester consists of three compulsory modules and one optional module. The second semester consists of the compulsory Project Preparation module and three optional modules.

Career Opportunities

Data scientists help organisations handle large amounts of data being produced thanks to digital technologies. Harvard Business Review described the role as 'The Sexiest Job of the 21st Century' due to the rare combination of skills that a trained data scientist possesses.

Data science has seen an unparalleled expansion as the data-driven economy grows. Increasingly organisations require skilled professionals who can handle large datasets and managers who can utilise the resulting analysis to make impactful decisions.

There is a range of potential jobs available; demand for big data staff is predicted to rise 92% over 5 years from Jan 2013. The programme provides an excellent opportunity for entry into data sciences or similar fields. Plus, big data positions offer a median salary of £55,000 – 24% higher than for IT staff in general (UK). There are also academic possibilities for doctoral study, as there are for entrepreneurial careers.

ECS runs a dedicated careers hub with is affiliated with more than 100 renowned companies such as IBM, Arm, Microsoft, Samsung, and Google. Visit our Careers Hub for more information.

Graduates from our MSc program can seek employment worldwide in:

  • established companies looking to spot trends in sales, marketing or operational data;
  • start-ups based around new opportunities in the booming data-driven economy;
  • government departments looking to utilise linked open data to gain insights to affect policy at the highest levels;
  • research/consultancy companies analysing data and feeding back to the wider community, with training and specialist services to clients.

Through an extensive blend of networks, mentors, societies and our on-campus startup incubator, we also support aspiring entrepreneurs looking to build their professional enterprise skills. Discover more about enterprise and entrepreneurship opportunities.

Fees & funding

Tuition fees

Fees for postgraduate taught courses vary across the University. All fees are listed for UK, EU and international full-time and part-time students alphabetically by course name.

View the full list of course fees


Scholarships, bursaries, sponsorships or grants may be available to support you through your course. Funding opportunities available to you are linked to your subject area and/or your country of origin. These can be from the University of Southampton or other sources.

Explore funding opportunities

Costs associated with this course

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

There will also be further costs for the following, not purchasable from the University:

Approved CalculatorsCandidates may use calculators in the examination room only as specified by the University and as permitted by the rubric of individual examination papers. The University approved models are Casio FX-570 and Casio FX-85GT Plus. These may be purchased from any source and no longer need to carry the University logo.
StationeryYou will be expected to provide your own day-to-day stationery items, e.g. pens, pencils, notebooks, etc). Any specialist stationery items will be specified under the Additional Costs tab of the relevant module profile.
TextbooksWhere a module specifies core texts these should generally be available on the reserve list in the library. However due to demand, students may prefer to buy their own copies. These can be purchased from any source.

Some modules suggest reading texts as optional background reading. The library may hold copies of such texts, or alternatively you may wish to purchase your own copies. Although not essential reading, you may benefit from the additional reading materials for the module.
Printing and Photocopying CostsIn the majority of cases, coursework such as essays; projects; dissertations is likely to be submitted on line. However, there are some items where it is not possible to submit on line and students will be asked to provide a printed copy. A list of the University printing costs can be found here:

In some cases you’ll be able to choose modules (which may have different costs associated with that module) which will change the overall cost of a programme to you. Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

Pre-course Reading List

We expect students to have a background in Computer Science or a related numerical discipline. To build knowledge around big data and data science ahead of the programme, we would recommend the following texts:

  • Schutt, R. and O'Neil, C., 2013. Doing data science: Straight talk from the frontline. "O'Reilly Media, Inc.”.
  • Davenport, T., 2014. Big data at work: dispelling the myths, uncovering the opportunities. Harvard Business Review Press.
  • Jurney, R., 2013. Agile Data Science: Building Data Analytics Applications with Hadoop. "O'Reilly Media, Inc.”.
  • Aiden, E. and Michel, J.B., 2013. Uncharted: Big data as a lens on human culture. Penguin.


Highfield Campus

Highfield is our main campus and the heart of the University. Set in beautiful green surroundings, it’s easily accessible from the city centre. University Road, Southampton, SO17 1BJ.

Find out more

Related courses

Share this courseFacebookTwitterWeibo