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.
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 program 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.
The MSc in Social Data Science is primarily assessed by essays that apply these methods to a substantive research question. This involves motivating the question with domain-level academic expertise and motivating the analysis with an understanding of the potential and limits of specific (usually computational) methodologies.
The three term MSc 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 formal requirements. The course is administered by the Oxford Internet Institute, a department within the Social Sciences Division. Teaching and supervision faculty are drawn from the department as well as a variety of departments around the University including but not limited to Engineering Science, Mathematics, Linguistics, Statistics, and Sociology. 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. During Michaelmas and Hilary terms, this 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 (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 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)
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.
Compulsory Intensive Courses
Three compulsory intensive courses run during the first term:
Fundamentals of Social Data Science
This course is a four week intensive primer to get people up to speed on programming in the Python programming language for use with data science.
Data Analytics at Scale
The course will teach computational complexity and how to profile and increase the computational efficiency of Python code. It will also cover parallel and distributed computing approaches, and discuss data storage and retrieval techniques.
Machine learning algorithms can discover patterns and hidden structure in data and use these for prediction of future data. This course covers the fundamentals of both supervised and unsupervised learning.
Compulsory Foundation Courses
Five compulsory foundation courses run over the course of the MSc. During the first term you will study Foundations of Social Data Science, and Applied Analytics Statistics. During the second term you will study Frontiers of Social Data Science, and Research Design for Social Data Science. Finally, during the third term you will take Foundations of Visualisation.
Foundations of Social Data Science
This course will introduce to some of the fundamental questions that have been raised in this domain across the social sciences.
Applied Analytics Statistics
Applied analytical statistics is a course focusing on the tools and techniques used by social scientists to understand, describe and analyse (quantitative) data.
Frontiers of Social Data Science
This course will take a look into the future and focus on the emerging role of data by looking at specific contexts and issues.
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.
Foundations of Visualisation
This course centres around discussion of the two-way interaction between visualisation and the social sciences.
You will take two option modules during the second term of the year. Option modules run for eight weeks. Recent option modules have included:
- Applied Machine Learning
- Digital Era Government and Politics
- Experiments in Social Data Science
- Fairness Accountability and Transparency in Machine Learning
- Human and Data Intelligence
- Internet Economics
- Introduction to Natural Language Processing for the Social Sciences
- Social Network Analysis and Interpretation
- Data-driven Network Science
Please note that not all options run every year.
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 keeping an eye on 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 at least one complete draft of your thesis prior to submission as well as additional advice on research design, data access, and analysis methods.
You will find a summary of how the different elements of the MSc are assessed below. Further information can be found on the course page of the department's website (please see Further information and enquiries).
First Term (Michaelmas)
- Introduction to Data Science and Machine Learning: short duration take-home submission assessing the content of the three compulsory intensive papers (Fundamentals of Social Data Science, Data Analytics of Scale, and Machine Learning)
- Compulsory foundation course Foundations of Social Data Science: submission (essay)
- Compulsory foundation course Applied Analytical Statistics: submission (research project)
Second Term (Hilary)
- Compulsory foundation course Frontiers of Social Data Science: submission (essay)
- Compulsory foundation course Research Design for Social Data Science: submission (essay)
- Option paper 1: submission (essay or project)
- Option paper 2: submission (essay or project)
You will be generally able to apply to access, review, and make use of resources from the available 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.
Third Term (Trinity)
- Compulsory foundation course Foundations of Visualisation: unassessed
In the third term, you will be assessed by a thesis on a topic of your choosing in consultation with your thesis 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 you will showcase your work in progress.
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 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, 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.
Entry requirements for entry in 2024-25
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 interactive tool to help you evaluate whether your application is likely to be competitive.
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.
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 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.
|Minimum overall score
|Minimum score per component
|IELTS Academic (Institution code: 0713)
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 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.
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
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 course 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 described 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. Whether or not you have secured funding will not be taken into consideration when your application is assessed.
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.
Initiatives to improve access to graduate study
This course is taking part in the 'Close the Gap' project which aims to improve access to doctoral study.
For this course, socio-economic data (where it has been provided in the application form) will be used to contextualise applications at the different stages of the selection process. Further information about how we use your socio-economic data can be found in our page about initiatives to improve access to graduate study.
Processing your data for shortlisting and selection
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:
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 MSc in Social Data Science is offered by the Oxford Internet Institute (OII) in partnership with Engineering Science, Sociology, Statistics, Mathematics, and other departments. Students at the department have access to IT infrastructure at both the departmental level and at the University level. This includes access to shared collaborative software, server space, and computing resources, including but not limited to ARC, Oxford’s high-performance computing cluster.
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.
Oxford Internet Institute
The Oxford Internet Institute (OII) is a dynamic and innovative department for research and teaching relating to the internet, located in a world-leading traditional research university. The multidisciplinary OII offers the opportunity to study academic, practical and policy-related issues that can only be understood by drawing on contributions from many different fields.
The OII is the only major department in a top-ranked international university to offer multidisciplinary courses in the social sciences dedicated to understanding the impact of the internet, data, and information technologies on society. We offer masters and doctoral level education across several degrees focused on social data science or the social science of the internet and technology.
Digital connections are now embedded in almost every aspect of our daily lives, and research on individual and collective behaviour online is crucial to understanding our social, economic and political world. As a fully multi-disciplinary department, we offer our students the opportunity to study academic, practical and policy-related issues and pursue cutting-edge research into the societal implications of the internet and digital technologies.
Our academic faculty and graduate students are drawn from many different disciplines: we believe this combined approach is essential to tackle society’s big questions. Together, we aim to positively shape the development of our digital world for the public good.
The University expects to be able to offer over 1,000 full or partial graduate scholarships across the collegiate University in 2024-25. 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 2024-25
Annual Course fees
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.
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 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 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. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.
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. For some courses, the department may have provided some additional advice below to help you decide.
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. You can use our interactive tool to help you evaluate whether your application is likely to be competitive.
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. Check the deadlines on this page and the information about deadlines in our Application Guide.
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.
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.
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.
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.
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, articulate a research question that speaks to an issue of relevance for social science research (generally within the computational social sciences).
Motivate the question with reference to academic literature on social data science or related computational work in social sciences such as sociology, economics, political science, social psychology, health informatics, or administration.
Posit how you would answer this question assuming access only to data that is publicly available or data that can be collected / labelled with modest effort and expense. Give examples of how access to computational resources can augment, alter, or enhance your proposed research. You may want to consider past literature, challenges with operationalisation, and the influence of sociodemographic or cultural considerations.
This question examines how effectively you translate topically relevant social science ideas into workable, focused concepts for data science research. Given the required brevity, you do not need to provide a working plan, analytical model, any empirical results, or any expanded references.
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 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.