About the course
The multidisciplinary MSc in Social Data Science provides the social and technical expertise needed to collect, critique, and analyse unstructured heterogeneous data about human behaviour, thereby informing our understanding of the social world.
This course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly. For this course, the socio-economic data you provide in the application form will be used to contextualise the shortlisting and decision-making processes. For more information see the full details about this pilot.
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; critiquing inequalities inherent in the data or manifested through analyses of this data, and developing the tools and techniques to make meaningful claims about the social world, through explanation, prediction and experimentation. 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.
Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, students continuing on to doctoral study have the option of taking a part-time DPhil.
The allocation of graduate supervision for this course 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.
Supervision for the MSc in Social Data Science spans multiple departments (please see the full list of faculty members eligible to supervise MSc students for this programme). The “course supervisor” is assigned early in Michaelmas term based on initial student interests and goals. The department reassesses supervision matching in Hilary term to ensure that student needs and skills are properly matched. The department also encourages students to adopt a co-supervisor where possible. Once the supervisor(s) are confirmed as “thesis supervisors” in Hilary term, the department expects an average of 6-8 meetings between the student and their supervisor(s) prior to submission of thesis. Supervisors are also responsible for giving written feedback on at least one complete draft of the student’s thesis prior to submission as well as additional advice on research design, data access, and analysis methods.
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 thesis supervisor. The thesis 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.
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 IBM, Google or Facebook, smaller start-ups firms like Academia.edu and Spotify, as well as regulatory positions such as the Office of National Statistics and Ministry of Justice, and various consultancies. MSc alumni have progressed to further graduate study at institutions such as Oxford, Harvard, Princeton, Columbia, and LSE among others.
The OII Alumni Wall features interviews from both MSc and DPhil alumni about their time at the Department and career paths after Oxford.
Changes to this course and your supervision
The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic (including Covid-19), epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.
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 illness, sabbatical leave, parental leave or change in employment.
Other courses you may wish to consider
If you're thinking about applying for this course, you may also wish to consider the courses listed below. These courses may have been suggested due to their similarity with this course, or because they are offered by the same department or faculty.
Courses suggested by the institute
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. 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.
All graduate courses offered by the Oxford Internet Institute
Oxford 1+1 MBA programme
This course can be studied as a part of the Oxford 1+1 MBA programme. The Oxford 1+1 MBA programme is a unique, two-year graduate experience that combines the depth of a specialised, one-year master’s degree with the breadth of a top-ranking, one-year MBA.
Entry requirements for entry in 2022-23
Proven and potential academic excellence
As a minimum, applicants should hold or be predicted to achieve the equivalent of the following UK qualifications:
- a first-class undergraduate degree with honours in any subject.
In exceptional circumstances, applicants with a distinguished record of workplace experience or other relevant achievements may be accepted with lower grades at undergraduate level. We nevertheless strongly encourage any applicants from industry to include at least one reference from an academic or someone in academic-related field.
For applicants with a degree from the USA, the minimum GPA sought is 3.7 out of 4.0.
If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.
GRE General Test scores
No Graduate Record Examination (GRE) or GMAT scores are sought.
Other qualifications, evidence of excellence and relevant 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.
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.
In almost all cases, Social Data Science will require the use of statistical or programmatic approaches. The OII teaches primarily in the Python programming language. Students in our MSc program will be taught in Python alongside other languages in specific circumstances where applicable. Applicants are not expected to have extensive programming skills in Python. It is strongly encouraged for applicants to have at least a working familiarity with the basics of programming, regardless of language.
Academic research related to data science or experience working in related businesses is not required, but may be an advantage.
English language proficiency
This course requires proficiency in English at the University's higher level. If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's higher level are detailed in the table below.
|Test||Minimum overall score||Minimum score per component|
|IELTS Academic (Institution code: 0713)||7.5||7.0|
TOEFL iBT, including the 'Home Edition'
(Institution code: 0490)
*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE)
†Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)
Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides further information about the English language test requirement.
Declaring extenuating circumstances
If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.
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
Candidates to the MSc in Social Data Science are not typically interviewed, except in exceptional circumstances where the admissions team need additional context from the applicant. If an interview is required, it is normally held three to six weeks after the application deadline. There is usually only one interview held, which lasts up to 30 minutes and can be held via video conferencing software. You will typically be asked to speak about research interests, reasons for applying, future career plans, and why you think this degree programme is the best way to continue your studies. It is more akin to a conversation than a test.
Any offer of a place is dependent on the University’s ability to provide the appropriate supervision for your chosen area of work. Please refer to the ‘About’ section of this page for more information about the provision of supervision for this course.
How your application is assessed
Your application will be assessed purely on academic merit and potential, according to the published entry requirements for the course. The After you apply section of this website provides further information about the academic assessment of your application, including the potential outcomes. Please note that any offer of a place may be subject to academic conditions, such as achieving a specific final grade in your current degree course. These conditions may vary depending upon your individual academic circumstances.
Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:
- Socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of the University’s pilot on selection procedures and for scholarships aimed at under-represented groups;
- Country of ordinary residence may be taken into account in the awarding of certain scholarships; and
- Protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.
Admissions panels and assessors
All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).
Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.
After an offer is made
If you receive an offer of a place at Oxford, your offer letter will give full details of your offer and any academic conditions, such as achieving a specific final grade in your current degree course. In addition to any academic conditions which are set, you will be required to meet the following requirements:
If you are offered a place, you will be required to complete a Financial Declaration in order to meet your financial condition of admission.
Disclosure of criminal convictions
In accordance with the University’s obligations towards students and staff, we will ask you to declare any relevant, unspent criminal convictions before you can take up a place at Oxford.
The MSc in Social Data Science is offered by the Oxford Internet Institute (OII) in partnership with Engineering Science, Sociology, Statistics, Mathematics, 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 regardless of gender identity, sexuality, physical mobility, ethnicity, or racial background. 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 OII's busy calendar of seminars and events showcases many of the most noteworthy people in Internet research, innovation and policy, allowing students to engage with the cutting 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.
The University expects to be able to offer around 1,000 full or partial graduate scholarships across the collegiate University in 2022-23. You will be automatically considered for the majority of Oxford scholarships, if you fulfil the eligibility criteria and submit your graduate application by the relevant December or January deadline. Most scholarships are awarded on the basis of academic merit and/or potential.
For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.
Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:
Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.
Further information about funding opportunities for this course can be found on the institute's website.
Annual fees for entry in 2022-23
Annual Course fees
Further details about fee status eligibility can be found on the fee status webpage.
Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges.
Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.
There are no compulsory elements of this course that entail additional costs beyond fees and living costs. However, as part of your course requirements, you will need to choose dissertation, project or thesis topics. Please note that, depending on your choice of topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.
Whilst many graduate students do undertake employment to support their studies, please remember that it is not recommended that MSc students take on even part-time employment during term-time. Within these limitations, some of the OII's existing MSc students have been employed on a short-term basis as Research Assistants on grant-funded projects, but only with the agreement of their supervisor, the MSc Course Convener and the Director of Graduate Studies.
For full information on employment whilst on course, please see the University's paid work guidelines for Oxford graduate students.
In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.
For the 2022-23 academic year, the range of likely living costs for full-time study is between c. £1,215 and £1,755 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. When planning your finances for any future years of study in Oxford beyond 2022-23, you should allow for an estimated increase in living expenses of 3% each year.
All graduate students at Oxford belong to a department or faculty and a college or hall (except those taking non-matriculated courses). If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. The Colleges section of this website provides information about the college system at Oxford, as well as factors you may wish to consider when deciding whether to express a college preference. Please note that ‘college’ and ‘colleges’ refers to all 45 of the University’s colleges, including those designated as Permanent Private Halls (PPHs).
For some courses, the department or faculty may have provided some additional advice below to help you to decide. Whatever you decide, it won’t affect how the academic department assesses your application and whether they decide to make you an offer. If your department makes you an offer of a place, you’re guaranteed a place at one of our colleges.
The following colleges accept students for full-time study on the MSc in Social Data Science:
How to apply
It is not necessary to contact academic staff before you apply.
The set of documents you should send with your application to this course comprises the following:
Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.
More information about the transcript requirement is available in the Application Guide.
A CV/résumé is compulsory for all applications. Most applicants choose to submit a document of one to two pages highlighting their academic achievements and any relevant professional experience.
Refer to OII course webpage
The personal statement for Social Data Science involves several brief essays on aspects of the course. Please follow the directions on the OII course webpage, which can be found on the Overview tab under the “How to Apply” heading. You will be directed to complete an online form, which will generate a PDF. You must then download this PDF and upload it to your application as your personal statement. Your statement should be written in English.
Your statement will be assessed for:
- evidence of aptitude using specific social science theories
- evidence of aptitude using mathematical and statistical techniques for the analysis of empirical data;
- evidence of interest in and understanding of multidisciplinary studies; and
- evidence of aptitude or skills in programming.
Your statement should focus on your academic achievements and research interests rather than personal achievements, interests and aspirations. When discussing the research of others you do not need to provide full references, but please be specific.
One essay, up to a maximum of 2,000 words, excluding any references and appendices
An academic essay or other writing sample from your most recent qualification, written in English, is required. If you have not previously written on areas closely related to the proposed research topic, you may provide written work on any topic that best demonstrates your academic abilities. The written work does not need to be data science related, but should demonstrate your critical and analytical capabilities and ability to present ideas clearly.
The word count does not need to include any bibliography or brief footnotes. Extracts of the required length that originally come from longer essays are also acceptable.
If possible, please ensure that the word count is clearly displayed on the document.
This will be assessed for:
- a comprehensive understanding of the subject area, including problems and developments in the subject;
- your ability to construct and defend an argument;
- your aptitude for analysis and expression; and
- your ability to present a reasoned case in proficient academic English.
References/letters of recommendation:
Three overall, academic and/or professional
Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.
Professional references are acceptable, particularly if you have been out of education for some time, but should focus particularly on your intellectual abilities rather than more narrowly on job performance.
Your references will be assessed for:
- your intellectual ability;
- your academic achievement; and
- your motivation and interest in the course and subject area.
Start or continue an application
Step 1: Read our guide to getting started, which explains how to prepare for and start an application.
Step 2: Check that you meet the Entry requirements and read the How to apply information on this page.
Step 3: Check the deadlines on this page and the deadline information in our Application Guide. Plan your time to submit your application well in advance - we recommend two or three weeks earlier.
Step 4: Check if you're eligible for an application fee waiver. Application fee waivers are available for:
- UK applicants from low-income backgrounds who meet the eligibility criteria;
- residents in a country on our low-income countries list (refer to the eligibility criteria);
- current Oxford graduate taught students applying for readmission to an eligible course; and
- additional applications to selected research courses that are closely related to your first application.
Step 5: Start your application using the relevant link below. As you complete the form, consult our Application Guide for advice at each stage. You'll find the answers to most common queries in our FAQs.