MSc in Social Data Science
About the course
The multidisciplinary MSc in Social Data Science provides the social and technical expertise needed to collect, critique, and analyse unstructured data about human behaviour.
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.
The growing field of social data science sits at the intersection of data science approaches to information retrieval, modelling, and prediction with social science approaches to theory-driven analysis, critiques of social processes, and linkages between policy and practice. The Social Data Science degree seeks students with training or a demonstrable aptitude for social science work and programming to refine and extend their skills through the generation, analysis, and critique of large-scale social data. The tools for such an approach are multifaceted and evolve quickly. Our programme embeds recent machine learning approaches to prediction, scalable strategies for ingesting and managing large scale data, analytical statistics for explanations, and specialist approaches such as computer vision, natural language processing, and network science. As a social science degree these approaches are generally applied to questions of social scientific relevance such as social inequality, censorship, hate speech, cohesion, and wellbeing.
Students will be expected to spend around 40 hours studying each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary Terms, MSc students are advised to allocate between 10 and 15 hours each week for each course they undertake.
In the first term (Michaelmas), this includes:
- At least 20 hours per week on reading, preparation and formative assignments (ten hours for the intensive course, five hours for each of the two foundation courses)
- 16 to 20 hours per week in classes (typically one and a half to two hours of lectures daily, one and a half to two hours of tutorials and practical exercises three-four days a week, plus additional seminars or workshops on certain courses)
In Hilary term, this equates to:
- At least 24 hours per week on reading, preparation and formative assignments (6 hours for each core/option course)
- Ten to 12 hours per week in classes (typically one and a half to two hours of lectures per course, plus a one hour seminar or workshop on certain core and methods-based courses)
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 course is primarily taught using the Python programming language with small exceptions for specialist work where necessary.
Supervision
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.
MSc students can expect to meet with their supervisor 8-10 times over the course of the degree. Students are assigned a course supervisor in their first term who will be the point of contact for keeping an eye on academic progress. In the second term (Hilary term), supervisory matching is reassessed to ensure that student needs and skills are properly matched. 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.
Assessment
MSc students take two foundation papers related primarily to social science theory and practice, and one intensive paper spanning programming, data science, and introductory machine learning during the first term. Two more foundation papers (a second theory course and a course on research design) occur in the second term. Additionally, in the second term, students write two option papers drawing from our range of option courses such as:
- Natural language processing,
- Data-driven network science,
- Fairness and accountability in Machine Learning,
- Internet economics,
- Applied machine learning.
As a student, you will be generally able to apply to access, review, and make use of resources from our option courses even if you don’t attend that specific course for credit. During each course you will receive regular feedback on formative exercises, assignments, and essays. This feedback does not count towards your final mark but prepares you for the graded summative work due after the completion of each course.
Finally in the third term, students are assessed by a thesis on a topic of their choosing in consultation with their academic supervisor. Planning for this thesis takes place in the second and third terms. In the third term this partially occurs through a non-graded research seminar where students showcase their work in progress.
Graduate destinations
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 technology companies such as IBM, Google or Meta; smaller start-ups like Academia.edu, Spotify, TikTok, and Bumble; and positions with regulators or government agencies globally. MSc alumni have progressed to further graduate study at institutions such as Cambridge, Harvard, Columbia, Princeton, Sciences Po, 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.
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.
For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.
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.
Statistical Science MSc
Statistics MSc by Research
Sociology MSc
Sociology and Demography MPhil
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 2023-24
Proven and potential academic excellence
Degree-level qualifications
As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:
- 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 programme 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) | 110 | Listening: 22 Reading: 24 Speaking: 25 Writing: 24 |
| C1 Advanced* | 191 | 185 |
| C2 Proficiency† | 191 | 185 |
*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.
References
You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The How to apply section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.
Supporting documents
You will be required to supply supporting documents with your application, including an official transcript and a CV/résumé. The How to apply section of this page provides details of the supporting documents that are required as part of your application for this course 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.
How your application is assessed
Your application will be assessed purely on your proven and potential academic excellence and other entry requirements published under that heading. References and supporting documents submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process.
An overview of the shortlisting and selection process is provided below. Our 'After you apply' pages provide more information about how applications are assessed.
Shortlisting and selection
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 selection procedure 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.
Whether or not you have secured funding will not be taken into consideration when your application is assessed.
Initiatives to improve access to graduate study
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 where it has been provided. Further details about this pilot, which applies to all applicants to this course, can be found in our pilot selection procedures section.
Processing your data for shortlisting and selection
Information about processing special category data for the purposes of positive action and using your data to assess your eligibility for funding, can be found in our Postgraduate Applicant Privacy Policy.
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.
Other factors governing whether places can be offered
The following factors will also govern whether candidates can be offered places:
- the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the About section of this page;
- the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
- minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.
Offer conditions for successful applications
If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our After you apply pages provide more information about offers and conditions.
In addition to any academic conditions which are set, you will also be required to meet the following requirements:
Financial Declaration
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.
Resources
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.
Funding
The University expects to be able to offer around 1,000 full or partial graduate scholarships across the collegiate University in 2023-24. 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.
Costs
Annual fees for entry in 2023-24
Fee status | Annual Course fees |
| Home | £25,760 |
| Overseas | £30,910 |
Further details about fee status eligibility can be found on the fee status webpage.
Information about course fees
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.
Where can I find further information about fees?
The Fees and Funding section of this website provides further information about course fees, including information about fee status and eligibility and your length of fee liability.
Additional information
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.
Employment
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.
Living costs
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 2023-24 academic year, the range of likely living costs for full-time study is between c. £1,290 and £1,840 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 2023-24, it is suggested that you allow for potential increases in living expenses of 5% or more each year – although this rate may vary significantly depending on how the national economic situation develops. UK inflationary increases will be kept under review and this page updated.
College preference
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:
Before you apply
Our guide to getting started provides general advice on how to prepare for and start your application. Check the deadlines on this page and the information about deadlines in our Application Guide. If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance.
Application fee waivers
An application fee of £75 is payable per course application. Application fee waivers are available for the following applicants who meet the eligibility criteria:
- applicants from low-income countries;
- refugees and displaced persons;
- UK applicants from low-income backgrounds; and
- applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.
You are encouraged to check whether you're eligible for an application fee waiver before you apply.
Do I need to contact anyone before I apply?
You do not need to make contact with the department before you apply but you are encouraged to visit the relevant departmental webpages to read any further information about your chosen course.
Completing your application
You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents. If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.
Referees:
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.
Official transcript(s)
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.
CV/résumé
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.
Personal statement:
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.
Written work:
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.
Start or continue your application
You can start or return to an application using the relevant link below. As you complete the form, please refer to the requirements above and consult our Application Guide for advice. You'll find the answers to most common queries in our FAQs.