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Degree type


Course length

1 year full-time


Durham City

Program code


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Course details

The Master of Data Science (Social Analytics) shares a common core with the other Master of Data Science programmes. Social analytics will create a new cohort of social scientists with the necessary skills to apply new computational methods to inform policy and business decisions and examine social phenomena and gain insight about the interactions between people and their social environment. It will also equip social scientists to work with social media and other new sources of data. While generic data science skills are useful for social scientists, interpreting social data comes with particular challenges. This programme includes modules about specialised methods and also the theoretical foundations to understand how to use them effectively.

Shared core modules with the suite of Data Science Master courses will ensure that you get equipped with the wider quantitative and computational skills required for your career. You will be carrying out team building activities, presenting case studies and carrying out both formative and summative assessments with students from all four faculties of Durham University, ensuring that you learn how to represent not just your own discipline but to also listen and integrate views and skills from other disciplines. An additional contribution to the academic environment will be provided by the Durham Research Methods Centre which will also help with the allocation of project topics through partnerships with local authorities, neighbouring NHS Trusts or other collaborators in the health and social care sectors.

All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites. Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.

Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage.

The Master of Data Science suite of programmes is a course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree that is not highly quantitative, including those in social sciences, the arts and humanities. Introductory modules are designed to bring students with non-technical degrees up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory. Core modules then introduce students to the full range of data science methods, building from well-known standard approaches to cutting-edge modern methods such as advanced causal inference techniques and deep learning. Optional modules allow students to focus on an area of interest.

The programme provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment. The broad aims are:

  • To develop advanced and systematic understanding of the complexity of data, including the sources of data relevant to science, alongside appropriate analysis techniques
  • To enable students to critically review and apply relevant data science knowledge to practical situations
  • To develop a critical awareness of current issues in data science which is informed by leading edge research and practice in the field
  • To develop a conceptual understanding of existing research and scholarship to enable the identification of new or revised approaches to data science practice
  • To develop creativity in the application of knowledge, together with a practical understanding of how established, advanced techniques of research and enquiry are used to develop and interpret knowledge in data science.
  • To develop the ability to conduct research into data science issues that requires familiarity with a range of data, research sources and appropriate methodologies and ethical issues
  • To develop advanced conceptual abilities and analytical skills in order to evaluate the rigour and validity of published research and assess its relevance to new situations
  • To extend the ability to communicate effectively both orally and in writing, using a range of media.

The programme is designed around a pedagogical framework which reflects the core categories of the data science discipline.

A number of subjects can be identified and defined within each application domain. Whilst a Masters programme cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the programme incorporates the necessary breadth and depth of material to ensure a skilled graduate.

The programme allows for progressive deepening in the students’ knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue.

The global dimension is reinforced through the use of international examples and case studies where appropriate.

Course Structure

Core modules:

The Master of Data Science (Social Analytics) programme is comprised of the following core modules:

  • Introduction to Computer Science (optional under certain circumstances)
  • Introduction to Statistics for Data Science
  • Introduction to Mathematics for Data Science (optional under certain circumstances)
  • Programming for Data Science
  • Social Science: Questions, Concepts, Theories, and Methods
  • Research Project

Examples of optional modules:

  • Computational Social Science
  • Machine Learning
  • Multilevel Modelling
  • Strategic Leadership
  • Text Mining and Language Analytics


The Master of Data Science suite of programmes is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science.

Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments. They are also developed through innovative teaching practices such as simulations. Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future.

All modules taught on this programme are underpinned by research, and embed elements of research training both in the delivery and in the assessment.

The Master of Data Science suite of programmes uses a wide range of learning and teaching methods:

  • Lectures
  • Seminars
  • Workshops
  • Computer/practical classes
  • Independent study, research and analysis
  • Structured reading
  • Case studies
  • Data Science Project
  • Supervisions
  • Group and individual oral presentations

The project is a major research project, conducted and written up as an independent piece of work with support from the student’s appointed supervisor.

Student academic support and guidance is provided through the members of the Management Board, module coordinators, and individual lecturers. This support may take the form of face-to-face contact, telephone, e-mail, or other online contact, as appropriate.

Information, requirements and expectations regarding the programme overall are provided in the course Sharepoint site. This is supplemented information on module aims/learning outcomes, content, key skills, formative and summative assessments and recommended reading.

Academic support to students is initially provided through an induction programme which provides an introduction to the University, the contributing departments, the programme, and key members of staff.

Entry requirements

A UK first or upper second class honours degree or equivalent in ANY degree that doesn’t include a strong data science component including those in social sciences, the arts and humanities, business, and sciences. Candidates with a degree in Social and Behavioural Sciences are strongly encouraged to apply.

Evidence of competence in written and spoken English if the applicant’s first language is not English:

  • minimum TOEFL requirement is 102 IBT (no element under 23)
  • minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent

English language requirements

Fees and funding

Full Time Fees

Tuition fees
Home students £12,900 per year
EU students £29,500 per year
Island students £12,900 per year
International students £29,500 per year

The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).

Please also check costs for colleges and accommodation.

Scholarships and Bursaries

We are committed to supporting the best students irrespective of financial circumstances and are delighted to offer a range of funding opportunities. 

Find out more about Scholarships and Bursaries

Career opportunities

Natural Sciences

Department information

Natural Sciences

Natural Sciences is the Faculty of Science “Department” that facilitates multidisciplinary and interdisciplinary degrees.

All around us, massive amounts of increasingly complex data are being generated and collected and business, research, government, communities, and families can use that data to make informed andrational decisions that lead to better outcomes. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.

For more information see our department pages.


  • 90% of courses are in the UK Top 10 in The Complete University Guide 2021.
  • Top 100 globally for employer reputation in the QS World University Rankings 2021.
  • Top 100 in the QS World University Rankings 2021.


The Master of Data Sciences degree programmes combines material from across departments. The email contact is

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