MSc in Social Data Science
About the course
The multidisciplinary MSc in Social Data Science welcomes students with an interest in applying quantitative and computational methods to questions of social and political significance for academics, policymakers, and the public.
The course is designed for students with some familiarity with programming and a strong background in social sciences, although applications are welcomed from all disciplinary backgrounds who meet the course entry requirements.
With the rapid expansion of big data and artificial intelligence (AI) in society there is a need both to understand how to best make use of these tools, as well as to consider their social implications from a practical and grounded perspective. This is an applied programme that combines machine learning, multivariate statistics, mixed-methods research, and a substantive focus on social, ethical, and legal considerations for AI and data broadly and for the governance and regulation of the internet more specifically.
It is an ideal course for ambitious students at the intersection of computing and the social sciences who are seeking careers with data in the public, private, and non-profit sectors.
You will join a cohesive cohort and will be expected to dedicate around 40 hours to this course each week during term, and to undertake further study and complete assessments during termly vacation periods. The OII's busy calendar of seminars and events showcases many of the most noteworthy people in internet research, innovation and policy, allowing you to engage with the cutting edge of scholarship and debates around internet technologies and AI.
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.
Course structure
An overview of the course structure is provided below. Details of the compulsory and optional elements of the course are provided in the Course components section of this page.
During the first term, you will receive core training in data science and machine learning, research design, and social science theory.
In the second term, you will receive further core training in applied social science theories and applied analytical statistics. You will also take two option courses of your choosing.
The third term is centred around the thesis, supported by cohort seminars and individual supervision. This independent research project of up to 12,000 words allows you to apply your learning to a topic of your choice.
During Michaelmas and Hilary terms, the study commitment equates to roughly 10 and 15 hours each week for each course taken.
In the first term (Michaelmas), this includes:
- At least 20 hours per week on reading, preparation and formative assignments
- 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 to four days a week, plus additional seminars or workshops on certain courses)
In the second (Hilary) term, this includes:
- 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)
Attendance
The course is full-time and requires attendance in Oxford. Full-time students are subject to the University's Residence requirements.
Resources to support your study
As a graduate student, you will have access to the University's wide range of resources including libraries, museums, galleries, digital resources and IT services.
The Bodleian Libraries is the largest library system in the UK. It includes the main Bodleian Library and libraries across Oxford, including major research libraries and faculty, department and institute libraries. Together, the Libraries hold more than 13 million printed items, provide access to e-journals, and contain outstanding special collections including rare books and manuscripts, classical papyri, maps, music, art and printed ephemera.
The University's IT Services is available to all students to support with core university IT systems and tools, as well as many other services and facilities. IT Services also offers a range of IT learning courses for students to support with learning and research, as well as guidance on what technology to bring with you as a new student at Oxford.
The OII is based in the Schwarzman Centre for the Humanities, a brand-new building at the University of Oxford opened in 2025. In addition to the OII, the building is home to seven humanities faculties and the Institute for Ethics in AI, a new library, an on-site cafe, a large number of well-equipped teaching and seminar rooms, and several performance and arts venues which are open to the public. The Oxford Internet Institute has its own centre and social hub within the building which facilitates interdisciplinary and collaborative work. At the heart of the building is the Great Hall, a large atrium which serves as a space for informal work, relaxation, meeting with friends, taking breaks and having refreshments.
The library, part of the Bodleian Libraries, houses the Oxford Internet Institute’s lending collection. There are 340 general reader seats, and around 80 graduate study seats – with a further 320 formal and informal study seats throughout the building outside the library. As well as the Library’s extensive staffed hours, there will be a 24/7 study space, including smart lockers for self-collect of borrowable items out of hours.
OII's MSc students are provided with hot-desk working space in the department. You will have access to OII's dedicated computing facilities and IT support, which includes collaborative software, server space, and computing resources, as well as access to ARC, Oxford's high-performance computing cluster.
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.
You can expect to meet with your supervisor eight to ten times over the course of the degree. You will be assigned a general supervisor in your first term who will be the point of contact for your academic progress. In the second term (Hilary term), you will be reassigned to a thesis supervisor in order to ensure that student needs and skills are properly matched.
Thesis supervisors are responsible for giving written feedback on your thesis prior to submission as well as additional advice on research design, data access, and analysis methods.
Assessment
MSc Social Data Science course assessment will either be conducted by timed examination or individual coursework submissions. Please note that the format of assessment for a particular course may change from year to year. The two main assessment periods are the winter vacation (December and January) and the spring vacation (March and April).
During each course you take 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.
The core course Introduction to Data Science and Machine Learning is currently assessed by a short-duration take-home paper. All other core and option courses are assessed by coursework, normally either an essay or research project.
In the third term, you will be assessed by a thesis on a topic of your choosing in consultation with a thesis supervisor. The thesis is the capstone to the MSc experience, providing students with the opportunity to apply the methods and approaches they have covered in the other parts of the course and carry out a substantive piece of academic research.
Graduate destinations
Employers recognise the value of a degree from the University of Oxford, and graduates from our existing courses 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 major companies such as Google, Meta, Spotify, TikTok, LinkedIn and Bumble; consultancy and other professional service functions; and positions with regulators or government agencies globally. MSc alumni have progressed to doctoral study at institutions such as Oxford, Cambridge, Harvard, Columbia, Princeton, Sciences Po, and LSE.
Accomplishments of recent MSc and DPhil alumni are highlighted on the study section of the OII website.
Changes to this course
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 if a pandemic, epidemic or local health emergency occurs. 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.
Course components
Core Courses
Across the first two terms you will take five compulsory core courses:
Introduction to Data Science and Machine Learning
Taught in the first term, this intensive course is divided into two taught modules:
- Fundamentals of Social Data Science in Python, an intensive programming primer to get people up to speed on the Python programming language for use with data science
- Introductory machine learning, covering the fundamentals of both supervised and unsupervised learning
Research Design for Social Data Science
This course introduces students to conceptual and methodological aspects of social science research methods, including both quantitative and qualitative methods.
Data and Society I
This course introduces students to some of the fundamental questions that have been raised in this domain across the social sciences, before taking a look into the future and focusing on the emerging role of data in specific contexts.
Applied Analytical Statistics
Applied analytical statistics is a course focusing on the tools and techniques used by social scientists to understand, describe and analyse (quantitative) data.
Data and Society II
This course looks to the future and focuses on the emerging role of data in specific contexts and issues. Each of these helps us to better grasp the multi-faceted nature of the evolving data age.
Option Courses
You will take two option modules during the second term of the year. Option modules run for eight weeks. Recent option modules have included:
- Algorithmic Fairness and Accountability
- Applied Machine Learning with Large Language Models
- Data-driven Network Science
- Internet Economics
- Social Data Science in Practice
Please note that not all options run every year.
Entry requirements for entry in 2026-27
Proven and potential academic excellence
The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our guidance to help you evaluate whether your application is likely to be competitive.
We know that contextual factors can make it difficult for candidates to demonstrate their full potential. This course is taking part in an initiative to use contextual data to help us to better understand your achievements in the context of your individual background. For further details, please refer to the information about improving access to graduate study in the How to apply section of this page.
Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. Contextual data may also be used in the assessment of studentships.
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. The OII 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 overall GPA that is normally required to meet the undergraduate-level requirement 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 on this MSc course will be taught in Python alongside other languages in specific circumstances where applicable. Applicants should have a demonstrated programming aptitude, as represented in university-level courses in Computer Science, Data Science, a subject-specific programming course (such as GIS) or demonstrated industry experience. Self-directed online courses will not be considered sufficient without applied research or industry experience in programming.
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 |
| Oxford Test of English Advanced | 165 | 155 |
*Changes to the TOEFL iBT test are being introduced on 21 January 2026. The University will not accept TOEFL tests taken from that date to meet the English language condition until a review of the revised test has been completed. Our Application Guide provides full details of the tests we accept.
†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. 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
Interviews are not normally held as part of the MSc Social Data Science admissions process.
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.
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.
Funding
For entry in the 2026-27 academic year, the collegiate University expects to offer over 1,100 full or partial graduate scholarships across a wide range of graduate courses.
If you apply by the January deadline shown on this page and receive a course offer, your application will then be considered for Oxford scholarships. For the majority of Oxford scholarships, your application will automatically be assessed against the eligibility criteria, without needing to make a separate application. There are further Oxford scholarships available which have additional eligibility criteria and where you are required to submit a separate application. Most scholarships are awarded on the basis of academic merit and/or potential.
To ensure that you are considered for Oxford scholarships that require a separate application, for which you may be eligible, use our fees, funding and scholarship search tool to identify these opportunities and find out how to apply. Alongside Oxford scholarships, you should also consider other opportunities for which you may be eligible including a range of external funding, loan schemes for postgraduate study and any other scholarships which may also still be available after the January deadline as listed on our fees, funding and scholarship search tool.
Details of college-specific funding opportunities can also be found on individual college websites:
Please refer to the College preference section of this page to identify which of the colleges listed above accept students for this course.
For the majority of college scholarships, it doesn’t matter which college, if any, you state a preference for in your application. If another college is able to offer you a scholarship, your application can be moved to that college if you accept the scholarship. Some college scholarships may require you to state a preference for that college when you apply, so check the eligibility requirements carefully.
Further information about funding opportunities for this course can be found on the institute's website.
Costs
Annual course fees
The fees for this course are charged on an annual basis.
Fees for the 2026-27 academic year at the University of Oxford
Fee status | Annual Course fees |
| Home | £28,170 |
| Overseas | £38,430 |
What do course fees cover?
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 costs information below.
How long do I need to pay 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 fees will usually increase annually, as explained in the University’s Terms and Conditions.
Where can I find more information about fees?
Our fees and other charges pages provide further information, including details about:
- course fees and fee liability;
- how your fee status is determined; and
- changes to fees and other charges.
Information about how much fees and other costs will usually increase each academic year is set out in the University's Terms and Conditions.
Additional costs
As part of your course requirements, you will need to choose a thesis topic. This element of the course is mandatory and forms part of the assessment for the course. Depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and materials. You are not formally required to undertake field research for your thesis, but if applicable to your choice of research topic, may also have to meet the costs of any fieldwork. You will need to meet these additional costs yourself, although you will have access to some departmental funding towards research expenses and may be able to apply for additional grants. Further information will be provided in the course handbook. There are no other compulsory elements of this course that entail additional costs beyond fees and living costs.
Living costs
In addition to your course fees and any additional course-specific costs, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.
Living costs for full-time study
For the 2026-27 academic year, the range of likely living costs for a single, full-time student is between £1,405 and £2,105 for each month spent in Oxford. We provide the cost per month so you can multiply up by the number of months you expect to live in Oxford. Depending on your circumstances, you may also need to budget for the costs of a student visa and immigration health surcharge and/or living costs for family members or other dependants that you plan to bring with you to Oxford (if dependant visa eligibility criteria are met).
Further information about living costs
The current economic climate and periods of high national inflation in recent years make it harder to estimate potential changes to the cost of living over the next few years. For study in Oxford beyond the 2026-27 academic year, it is suggested that you budget for potential increases in living expenses of around 4% each year – although this rate may vary depending on the national economic situation.
A breakdown of likely living costs for one month during the 2026-27 academic year are shown below. These costs are based on a single, full-time graduate student, with no dependants, living in Oxford.
| Lower range | Upper range | |
|---|---|---|
| Food | £315 | £545 |
| Accommodation | £825 | £990 |
| Personal items | £160 | £310 |
| Social activities | £50 | £130 |
| Study costs | £35 | £90 |
| Other | £20 | £40 |
| Total | £1,405 | £2,105 |
For information about how these figures have been calculated as well as tables showing the likely living costs for nine and twelve months, please refer to the living costs page of our website.
College preference
Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs).
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. Before deciding, we suggest that you read our brief introduction to the college system at Oxford and our advice about expressing a college preference.
If you are a current Oxford student and you would like to remain at your current Oxford college, you should check whether it is listed below. If it is, you should indicate this preference when you apply. If not, you should contact your college office to ask whether they would be willing to make an exception. Further information about staying at your current college can be found in our Application Guide.
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, including advice to help you evaluate whether your application is likely to be competitive.
If it is important for you to have your application considered under a particular deadline – eg under the 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. Check the deadlines on this page and the information about deadlines and when to apply in our Application Guide.
Application fee waivers
An application fee of £75 is payable for each application to this course. 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 contact the department before you apply but you are encouraged to visit the relevant departmental webpages to read any further information about your chosen course.
Improving access to graduate study
This course is taking part in initiatives to improve the selection procedure for graduate applications, to ensure that all candidates are assessed fairly.
Contextual data (where it has been provided in the application form) will be used as part of an initiative to contextualise applications at the different stages of the selection process.
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.
For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application.
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.
Personal statement:
Response to two essay prompts of a maximum of 500 words each
For the personal statement for this course, you must submit your responses to two essay prompts as a single combined document with clear sub-headings. Please ensure that the word counts are clearly visible in the document. Your statement should be written in English.
Please answer the following questions in no more than 500 words per question. Each question should be treated as a short standalone essay and you should avoid referring to answers to other questions in your responses.
Question 1 (500 words)
In no more than 500 words, propose a brief research plan to address a question of interest within the social sciences. This plan should include the following:
- An introductory statement that identifies either a novel phenomenon or gap within the existing literature;
- A clearly defined research question or hypothesis that will focus this work. This should ideally draw on literature in social sciences such as sociology, economics, political science, social psychology, health informatics, or administration;
- A clarification of how one would access data and operationalise any key concepts that will be measured; and
- Any relevant limitations on data access, ethics, or computational resources that should be considered.
This is not a full research plan and need not be overly focused on the specific practical details. We are interested in learning about your topic(s) of interest, how you operationalise phenomena for analysis, and how you might approach this task given the practical constraints and ethical expectations of doing academic research.
Question 2 (500 words)
Social data science relies heavily on both inferential statistics and machine learning methods. While our programme teaches many of the basics, it does so at an accelerated pace with assumptions of some prior experience with programming, maths, and statistics.
In no more than 500 words, please explain how your past learning has prepared you for this programme with emphasis on quantitative and technical skills. Please describe:
- Any courses or work experience with programming and/or quantitative reasoning, particularly as mentioned on a submitted transcript;
- Any creative, autonomous, or collaborative projects where you applied these skills (as opposed to strict tutorials); and
- Relevant future ambitions for your learning journey.
This question examines technical readiness so please be specific when describing your use of packages, scientific computing methods, or models.
Your statement will be assessed for:
- evidence of aptitude applying social science theories to empirical often large-scale data;
- evidence of aptitude using mathematical and statistical techniques for the analysis of empirical data;
- application of critical thinking to existing paradigms, models, or approaches;
- evidence of interest in and understanding of multidisciplinary studies; and
- evidence of aptitude or skills in programming.
Your response should focus on your academic achievements and research interests, rather than personal interests and aspirations. Given the required brevity, when referencing citations in line you do not need to provide the full references as part of your response to either question.
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. Written work must be entirely your own work except where clearly indicated; supporting quotations from any work authored by others must be properly identified and referenced. It is recommended that you do not submit co-authored work, as the academic assessors will find it difficult to evaluate your specific input. If you do submit co-authored work, you should include the full list of authors and clearly describe your own contribution at the top of the written work document.
If possible, please ensure that the word count is clearly displayed on the document.
Written work 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.
After you've submitted your application
Your application (including the supporting documents outlined above) will be assessed against the entry requirements detailed on this course page. Whether or not you have secured funding will not be taken into consideration when your application is assessed. You can find out more about our shortlisting and selection process in our detailed guide to what happens next.
Find out how to manage your application after submission, using our Applicant Self-Service tool.