MSc in Social Data Science
The multidisciplinary MSc in Social Data Science provides the social and technical expertise needed to analyse unstructured heterogeneous data about human behaviour, thereby informing our understanding of the human world.
Social data generated digitally (from, for example, social media, communications platforms, Internet of Things [IoT] devices, sensors/wearables, and mobile phones) offer a way to accumulate new large-scale data, in addition to existing data that have been converted to digital formats. These data can be put to work helping us understand big issues of crucial interest to the social sciences, industry, and policy-makers including social, economic and political behaviour, interpersonal relationships, market design, group formation, identity, international movement, ethics and responsible ways to enhance the social value of data, and many other topics.
The growing field of social data science involves developing the science of these social data: creating viable datasets out of messy, real world data; and developing the tools and techniques to analyse them to tell us something about the world, through explanation, prediction and the testing of interventions. In this way, social data science offers a data science where the data relates to individual and social behaviour and a social science with generation and analysis of real-time transactional data at its centre.
You will take a combination of core and option papers and produce a dissertation of up to 15,000 words with the support of a dissertation supervisor. The dissertation provides you the opportunity to apply the methods and approaches you have covered in the other parts of the course and carry out a substantive piece of academic research.
In addition to this full-time taught course, the MSc in Social Data Science is also offered as part of a combined taught and research (1+3) programme, for students wishing to continue on to doctoral study (with the DPhil in Social Data Science commencing in the 2019-2020 academic year). Applicants interested in the combined programme should consult the MSc + DPhil in Social Data Science course page, which provides information about the course and details of how to apply.
Employers recognise the value of a degree from the University of Oxford, and graduates from our existing programmes have secured excellent positions in industry, government, NGOs, or have gone on to pursue doctoral studies at top universities. For example, non-academic destinations of recent graduates have included large Internet companies such as Google or Facebook, smaller start-ups like Academia.edu, as well as regulatory positions and consultancy. MSc alumni have progressed to further graduate study at institutions such as Oxford, Harvard, Princeton and LSE.
The OII Alumni Wall features interviews from both MSc and DPhil alumni about their time at the Department and career paths after Oxford.
Entry requirements for entry in 2018-19
Within equal opportunities principles and legislation, applications will be assessed in the light of an applicant’s ability to meet the following entry requirements:
1. Academic ability
Proven and potential academic excellence
Applicants are normally expected to be predicted or have achieved a first-class or strong upper second-class undergraduate degree with honours (or equivalent international qualifications), as a minimum, in any subject.
For applicants with a degree from the USA, the minimum GPA sought is 3.5 out of 4.0.
If you hold non-UK qualifications and wish to check how your qualifications match these requirements, you can contact the National Recognition Information Centre for the United Kingdom (UK NARIC).
No Graduate Record Examination (GRE) or GMAT scores are sought.
Demonstrated quantitative aptitude or experience
Applicants are normally expected to demonstrate quantitative aptitude or experience in introductory calculus and matrix algebra, equivalent to, for example:
- A-levels mathematics
- Mathematical Studies SL from the International Baccalaureate Diploma Programme
- or Advanced Placement (AP) Calculus AB.
Applicants may demonstrate this aptitude/experience in a variety of ways including:
- undergraduate transcripts with a strong pass for Probability, Statistics, Linear Algebra, and/or Calculus;
- an A or A* rating for A-level mathematics;
- a score of 4 or 5 on the AP Calculus AB or BC exam; or
- evidence of the successful completion of online courses with similar content.
Other appropriate indicators will include:
You will be required to supply supporting documents with your application, including references and an official transcript. See 'How to apply' for instructions on the documents you will need and how these will be assessed.
Performance at interview(s)
Interviews are held as part of the admissions process, although some students will be admitted without an interview. If a student clearly exceeds all the admission criteria and their proposed research is innovative and can be supervised by faculty of the programme then they may be made an offer without an interview.
If an interview is required, it is normally held three to six weeks after the application deadline. Interviews can be done in person, by telephone or via Skype with or without video. There is usually only one interview held which lasts up to 30 minutes. You will be asked questions about research interests, future career plans, and why you think this degree programme is the best way to continue your studies.
Applicants are not expected to have published academic work previously, although publication may help the assessors judge your writing ability and thus could help your application.
Other qualifications, evidence of excellence and relevant experience
Academic research related to data science or experience working in related businesses is not required, but may be an advantage.
2. English language requirement
Applicants whose first language is not English are usually required to provide evidence of proficiency in English at the higher level required by the University.
3. Availability of supervision, teaching, facilities and places
The following factors will govern whether candidates can be offered places:
- The ability of the Oxford Internet Institute and collaborating departments to provide the appropriate supervision, research opportunities, teaching and facilities for your chosen area of work.
- Minimum and maximum limits to the numbers of students who may be admitted to Oxford's research and taught programmes.
The provision of supervision, where required, is subject to the following points:
- The allocation of graduate supervision is the responsibility of the Oxford Internet Institute and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff.
- Under exceptional circumstances a supervisor may be found outside the Oxford Internet Institute.
Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include sabbatical leave, maternity leave or change in employment.
4. Disability, health conditions and specific learning difficulties
Students are selected for admission without regard to gender, marital or civil partnership status, disability, race, nationality, ethnic origin, religion or belief, sexual orientation, age or social background.
Decisions on admission are based solely on the individual academic merits of each candidate and the application of the entry requirements appropriate to the course.
Further information on how these matters are supported during the admissions process is available in our guidance for applicants with disabilities.
All recommendations to admit a student involve the judgment of at least two members of academic staff with relevant experience and expertise, and additionally must be approved by the Director of Graduate Studies or Admissions Committee (or equivalent departmental persons or bodies).
Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.
The MSc in Social Data Science is offered by the Oxford Internet Institute (OII) in partnership with Statistics, Engineering, Sociology, and other departments. The OII faculty works at the cutting-edge of their fields, and this innovative research is fully reflected in their course teaching. The department prides itself on providing a stimulating and supportive environment in which all students can flourish. As a fully multidisciplinary department, the OII offers you the opportunity to study academic, practical and policy-related issues that can only be understood by drawing on contributions from across many different fields.
The department's busy calendar of seminars and events brings many of the most important people in Internet research, innovation and policy to the OII, allowing students to engage with the 'bleeding edge' of scholarship and debates around the Internet.
OII students also take full advantage of the substantial resources available at the University of Oxford, including world-leading research facilities and libraries, and a buzzing student scene. The departmental library provides students access to a range of resources including the texts required for the degree. Other University libraries provide valuable additional resources of which many students choose to take advantage.
For more information please click "Further Official Information" below.
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