Electronics and Computer Science (ECS), University of Southampton

Electronics and Computer Science (ECS)

MSc Data Science

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.

To Apply

You can apply for the programme through the University of Southampton's online postgraduate application system. For more background and detailed information, see How to Apply.

Please note we are part of the Faculty of Physical Sciences and Engineering (FPSE).

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. 

Key Facts

  • Our academics are playing a leading role in establishing a European Data Science Adcademy
  • 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)
  • We are ranked in the UK top ten for Computer Sciences by the 2016 Guardian and Times / Sunday Times Good University Guides
  • Southampton is ranked in the top 51-100 universities for Computer Science in the 2015 QS World Rankings
  • Our scientists have pioneered many of the most important advances in Computer Science and Web technology of the past 10 years, including developments around Open Data, Semantic Web and Linked Data.

Entry requirements

Honours Degree:

Our normal entry requirement is an upper second-class honours degree (or equivalent) in computer science or a related numerical discipline such as mathematics, engineering or management sciences.

English Language Requirements:

If English is not your first language, you will be required to pass an approved English test. We normally ask for IELTS 6.5 overall with at least 6.0 in each competency. For information on other accepted English language tests, please visit www.southampton.ac.uk/admissions_language.

International Qualifications:

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

Please note we are part of the Faculty of Physical Sciences and Engineering (FPSE).

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.


Year 1

The program consists of five compulsory modules spread across two semesters, each worth 7.5 ECTS credits, and an individual project worth 30 ECTS credits. The compulsory modules cover data analysis and use, as well as project preparation.

You can also choose from a wide range of optional topics, including advanced topics of data processing and manipulation, data analysis, and data use, and applications, allowing you to structure the program according to your strengths and preferences. These optional modules should add up to a minimum of 22.5 ECTS credits.

Modules not in Semesters

Compulsory Modules


To Apply

You can apply for the programme through the University of Southampton's online postgraduate application system. For more background and detailed information, see How to Apply.

Please note we are part of the Faculty of Physical Sciences and Engineering (FPSE).

Related programmes

Career opportunities

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.

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.

Pre-course reading lists

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.