social data science phd oxford

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DPhil in Social Data Science

University of oxford, different course options.

• Key information

Course Summary

Tuition fees, entry requirements, similar courses at different universities, key information data source : idp connect, qualification type.

PhD/DPhil - Doctor of Philosophy

Subject areas

Data Science

Course type

About the course

The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.The DPhil in Social Data Science at the Oxford Internet Institute (OII) will introduce you to cutting-edge research whilst studying in a beautiful, historic setting that is both student- and family-friendly. During your study at Oxford, you are encouraged to pioneer new approaches to contemporary social and policy issues online, developing new computational and data-driven methodology to inform the development and governance of technology. As a student, you will be part of a diverse cohort of research students, of many nationalities and from a wide range of scientific backgrounds. Research students in Social Data Science are graduates in subjects from computer science and mathematics to physics, as well as transdisciplinary subjects such as human-centred data science and complex systems.

The course combines individual supervision with a selection of lectures, seminars, transferrable skills training, and opportunities to participate in leading-edge research activities. OII faculty are world class experts working in the cutting-edge of their fields, and this innovative research is fully reflected in their course teaching. You will be able to audit courses led by faculty at the OII, as well as courses in other departments.

The programme provides a strong computational foundation, training you to develop new research skills in areas such as machine learning, statistical modelling, large-scale data collection, algorithm auditing, or network science. The DPhil in Social Data Science provides you with a rare grounding in both technical skills and social science research , helping you build critical skills to study digital technologies. There are weekly opportunities for you to interact with DPhil in Information, Communication and the Social Sciences students, providing a rich multidisciplinary environment.

As a full-time student, you are expected to continue working outside of the University terms with an annual holiday of approximately eight weeks.

Part-time study

The DPhil programme at the OII is also available on a part-time basis. The part-time programme is spread over six to eight years of study and research. It offers the flexibility of part-time study with the same high standards and requirements as the full-time DPhil programme. The part-time DPhil also provides an excellent opportunity for professionals in industry and civil society to undertake rigorous long-term research that may be relevant to their career.

As a part-time student, you will be required to attend seminars, supervision meetings, and other obligations in Oxford for a minimum of 30 days each year. Attendance will be required during term-time (a minimum of one day each week). There will be limited flexibility in the dates and pattern of attendance, which will normally be determined by the fixed teaching and seminar schedule during term. Attendance may be required outside of term-time on dates to be determined by mutual agreement with your supervisor. You will have the opportunity to tailor your part-time study in liaison with your supervisor and agree your pattern of attendance.

UK fees Course fees for UK students

For this course (per year)

International fees Course fees for EU and international students

As a minimum, applicants should hold or be predicted to achieve the equivalent of the following UK qualifications or their equivalent: a master's degree with a mark of at least 65%; and a first-class or strong upper second-class undergraduate degree with honours in any subject. It is expected that all applicants will hold a taught masters or other advanced degree. For applicants with a degree from the USA, the minimum GPA sought is 3.5 out of 4.0.

MSc Applied Data Science (Conversion)

Anglia ruskin university, msc data science and artificial intelligence, bournemouth university, msc data science, london south bank university, msc data science - 18 months, university of east anglia uea.

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Social Data Science

This Social Data Science degree at the University of Oxford will train individuals to develop and adapt techniques such as machine learning to analyse large, structured and unstructured, complex datasets in order to improve decision making and answer social science research questions.

University of Oxford Multiple locations Oxford , England , United Kingdom Top 0.1% worldwide Studyportals University Meta Ranking 4.1 Read 166 reviews

Features 

• The DPhil in Social Data Science at the University of Oxford provides an opportunity for students to formulate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Engineering Science, Statistics, Sociology, Computer Science and other departments across the University of Oxford, as well as by the complementary strengths of the student cohort, who draw upon expertise and experience from across the disciplinary spectrum.

Programme Structure

Research 

• Beyond the technical skills, the programme will also provide students with a solid grounding in social science theory and methodology, and reflection on the consequences of the techniques applied. 
• Ordinarily, students will be expected to have training similar to that offered within the MSc in Social Data Science, but gaps may be filled with courses from the MSc curriculum as well as other existing courses at the University.
• Over the course of the three to four years, you are expected to produce an important and original piece of scholarship that will make a significant contribution to the dynamic area of Social Data Science. On completion, you will have the qualities and transferable skills necessary to excel in teaching, research, policymaking or business.

Key information

• 36 months

Start dates & application deadlines

• Apply before 2025-01-05 00:00:00

Disciplines

Academic requirements, english requirements, student insurance.

Make sure to cover your health, travel, and stay while studying abroad. Even global coverages can miss important items, so make sure your student insurance ticks all the following:

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Other requirements

General requirements.

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

• a master's degree with a mark of at least 65%; and
• a first-class or strong upper second-class undergraduate degree with honours in any subject.

Tuition Fee

International.

Part-time study:

• Home: £6,785
• Overseas: £14,570

Living costs for Oxford

The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.

In order for us to give you accurate scholarship information, we ask that you please confirm a few details and create an account with us.

Scholarships Information

Below you will find PhD's scholarship opportunities for Social Data Science.

Available Scholarships

You are eligible to apply for these scholarships but a selection process will still be applied by the provider.

Read more about eligibility

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DPhil in Social Data Science University of Oxford

Course options

Qualification.

PhD/DPhil - Doctor of Philosophy

University of Oxford

• TUITION FEES
• ENTRY REQUIREMENT
• UNIVERSITY INFO

Course summary

About the course

The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.The DPhil in Social Data Science at the Oxford Internet Institute (OII) will introduce you to cutting-edge research whilst studying in a beautiful, historic setting that is both student- and family-friendly. During your study at Oxford, you are encouraged to pioneer new approaches to contemporary social and policy issues online, developing new computational and data-driven methodology to inform the development and governance of technology. As a student, you will be part of a diverse cohort of research students, of many nationalities and from a wide range of scientific backgrounds. Research students in Social Data Science are graduates in subjects from computer science and mathematics to physics, as well as transdisciplinary subjects such as human-centred data science and complex systems.

The course combines individual supervision with a selection of lectures, seminars, transferrable skills training, and opportunities to participate in leading-edge research activities. OII faculty are world class experts working in the cutting-edge of their fields, and this innovative research is fully reflected in their course teaching. You will be able to audit courses led by faculty at the OII, as well as courses in other departments.

The programme provides a strong computational foundation, training you to develop new research skills in areas such as machine learning, statistical modelling, large-scale data collection, algorithm auditing, or network science. The DPhil in Social Data Science provides you with a rare grounding in both technical skills and social science research , helping you build critical skills to study digital technologies. There are weekly opportunities for you to interact with DPhil in Information, Communication and the Social Sciences students, providing a rich multidisciplinary environment.

As a full-time student, you are expected to continue working outside of the University terms with an annual holiday of approximately eight weeks.

Part-time study

The DPhil programme at the OII is also available on a part-time basis. The part-time programme is spread over six to eight years of study and research. It offers the flexibility of part-time study with the same high standards and requirements as the full-time DPhil programme. The part-time DPhil also provides an excellent opportunity for professionals in industry and civil society to undertake rigorous long-term research that may be relevant to their career.

As a part-time student, you will be required to attend seminars, supervision meetings, and other obligations in Oxford for a minimum of 30 days each year. Attendance will be required during term-time (a minimum of one day each week). There will be limited flexibility in the dates and pattern of attendance, which will normally be determined by the fixed teaching and seminar schedule during term. Attendance may be required outside of term-time on dates to be determined by mutual agreement with your supervisor. You will have the opportunity to tailor your part-time study in liaison with your supervisor and agree your pattern of attendance.

Tuition fees

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£ 29,140 per year

Tuition fees shown are for indicative purposes and may vary. Please check with the institution for most up to date details.

University information

University league table, campus address.

University of Oxford, University Offices, Wellington Square, Oxford, Oxfordshire, OX1 2JD, England

Subject rankings

Subject ranking.

2nd out of 117

Entry standards

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University of oxford: social data science (1+3/1+6).

The information provided on this page was correct at the time of publication (November 2022). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.

The DPhil in Social Data Science provides an opportunity for students to formulate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Engineering Science, Statistics, Sociology, Computer Science and other departments across the University of Oxford, as well as by the complementary strengths of the student cohort, who draw upon expertise and experience from across the disciplinary spectrum.

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.

Students are encouraged to pioneer new approaches to contemporary social problems, exploiting fast expanding possibilities in large-scale data collection, machine learning, and statistical modelling. This degree will train individuals to develop and adapt techniques such as machine learning to analyse large, structured and unstructured, complex datasets in order to improve decision making and answer social science research questions.

Beyond the technical skills, the programme will also provide students with a solid grounding in social science theory and methodology, and reflection on the consequences of the techniques applied. Ordinarily, students will be expected to have training similar to that offered within the MSc in Social Data Science, but gaps may be filled with courses from the MSc curriculum as well as other existing courses at the University.

Over the course of the three to four years, you are expected to produce an important and original piece of scholarship that will make a significant contribution to the dynamic area of Social Data Science. On completion, you will have the qualities and transferable skills necessary to excel in teaching, research, policymaking or business.

The DPhil programme at the Oxford Internet Institute (OII) is also available on a part-time basis. The part-time programme is spread over six to eight years of study and research. The part-time degree offers the flexibility of part-time study with the same high standards and requirements as the full-time DPhil programme. The part-time DPhil also provides an excellent opportunity for professionals in high tech industries to undertake rigorous long-term research that may be relevant to their working life. Please visit the department website for further details on part-time doctoral study or contact the Graduate Studies Assistant.

As a part-time student you will be required to attend seminars, supervision meetings and other obligations in Oxford for a minimum of 30 days each year. Attendance will be required during term-time a minimum of one day each week. There will be limited flexibility in the dates and pattern of attendance, which will normally be determined by the fixed teaching and seminar schedule during term.A ttendance may be required outside of term-time on dates to be determined by mutual agreement with your supervisor. You will have the opportunity to tailor your part-time study in liaison with your supervisor and agree your pattern of attendance.

Mixed Mode, 4 years started Oct 2023

Mixed Mode, 7 years started Oct 2023

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The DPhil in Social Data Science is an advanced research degree which provides the opportunity to investigate and address novel research questions at the intersection of the computational and social sciences, supported by the multidisciplinary faculty at the OII, Mathematics, Computer Science, Engineering, Statistics, and other departments across the University of Oxford. The DPhil, normally taking three to four years of full-time study to complete, is known as a PhD at other universities.

The DPhil in Social Data Science at the Oxford Internet Institute (OII) will introduce you to cutting-edge research whilst studying in a beautiful, historic setting that is both student- and family-friendly.

During your study at Oxford, you are encouraged to pioneer new approaches to contemporary social and policy issues online, developing new computational and data-driven methodology to inform the development and governance of technology.

PhD Program Requirements

A master’s degree with a mark of at least 65%; and

A first-class or strong upper second-class undergraduate degree with honours in any subject.

It is expected that all applicants will hold a taught masters or other advanced degree.

For applicants with a degree from the USA, the minimum GPA sought is 3.5 out of 4.0.

Strong analytical abilities in understanding the social aspects of the internet, World Wide Web and related technologies, as shown by the candidate’s writing sample and/or the reports of referees, are required.

It would be expected that graduate applicants would be familiar with the recent published work of their proposed supervisor.

PhD Funding Coverage

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.

Application Requirement

1. Online Application

2. 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.

3. Personal statement: Your statement should explain your motivation for applying for the DPhil course at Oxford and the specific research areas that interest you and/or you intend to specialise in. It should focus on your academic achievements and research interests rather than personal achievements, interests and aspirations. You should also include details of any relevant experience in engaging in social data science related research.

4. Research proposal: A coherent thesis proposal is required in an area of study covered by at least one member of the research staff within the Social Data Science programme. Your proposal should focus on specific research you propose to undertake rather than personal achievements, interests and aspirations.

5. Written work: 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.

6. GRE General Test scores: No Graduate Record Examination (GRE) or GMAT scores are sought.

7. 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.

Application Deadline

5 January 2025

Application Fee

An application fee of £75 is payable per course application.

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DPhil students

Support for dphil students.

Researcher Development is a team within the Social Sciences Division (SSD) responsible for delivering transferable skills training and development opportunities to SSD doctoral researchers and early career researchers. We complement and supplement discipline-specific training within departments.

Use the filters below to explore the resources, events, opportunities, and networks available.

Making the most of what we have to offer

1) Read the termcard at the beginning of the term and make your development plan

Events tend to book out quickly, so act sooner rather than later!

2) Read the Researcher Development term-time weekly email newsletter

For running updates including additions to the termcard, or places having become available on courses through cancellations.

3) Get in touch if you’ve got a training, networking or event idea you’d like to discuss

We’re keen to support researcher-led initiatives where possible, and there are a range of ways in which we may be able to work together.

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2023 Oxford Social Data Science OII

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MSc in Social Data Science

Page Contents

Introduction, key information, student experience, supervisors, fees & funding.

The multi-disciplinary MSc in Social Data Science equips students with applied expertise in rapidly advancing domains in machine learning coupled with theories, practices, and research focus from the social sciences.

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 and approaches, as well as to consider their social implications from a practical and grounded perspective. These new approaches can provide new perspectives on classic questions in political science, law, or sociology. At the same time, the technologies behind these approaches are rapidly posing new questions of their own regarding identity, ethics, privacy, relationships, human rights, commerce, and health, with importance for societies, regulatory bodies, and states.

During this degree, students will collect, combine, and interrogate social and behavioural data from a variety of social science perspectives with an emphasis on quantitative and computational skills alongside best practices in scientific inquiry and ethical research. The course is administered by the Oxford Internet Institute within the Department of Social Science with additional teaching and supervision from faculty in departments across the university including Mathematics, Engineering Sciences, and Statistics, as well as Linguistics, Economics, and Sociology.

Students 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.

Social Data Science students take five compulsory foundation papers, a data science intensive with three modules, and two options papers. Students finish by submitting a thesis of up to 15,000 words on topic chosen by the student in consultation with their thesis supervisor.

• Foundation papers cultivate core skills, methods, theories and concepts required for sophisticated study in the field.
• The Introduction to Data Science and Machine Learning paper consists of three short intensive courses designed to introduce programming skills for data capture and cleaning, fundamentals of statistical and machine learning, and approaches for scaling up data collection and analyses.
• Option papers enable students to develop in-depth specialist techniques and disciplinary expertise.
• The thesis assesses a student’s ability to complete an empirical research project, providing a realistic example of the challenges faced in data science settings in academia and industry.

The programme combines traditional lectures with computer lab sessions and hands-on mathematics and programming exercises.

The MSc in Social Data Science is designed for:

• Social science students with existing quantitative and programming skills who wish to further develop their skills for analysing structured and unstructured data using advanced computational and statistical techniques to address a social science topic.
• Students from the social sciences looking to transition into research at the intersection of the social and computational sciences.
• Experienced data analysts and consultants with an interest in applying quantitative or computational approaches to social science research questions about or using machine learning.
• Students wishing to work in data analytics, business analytics, and other data-intensive roles that combine writing and interpretation with data analysis.

Learning outcomes

Upon completion of the MSc in Social Data Science will have:

• Developed an appreciation of how theories, methods, and practices from the social sciences and data science approaches to research can be mutually informative.
• Designed a research project that applies tools and methods from data science to address a social science research question.
• Compared and evaluated multiple computational approaches to a research question and chosen the most appropriate with due consideration to data access, computational resources, research ethics, and the state of academic knowledge.
• Communicated across disciplines and explained research outcomes in an accessible language and to a wide audience.
• Obtained a critical understanding of the uses and limitations of current computational approaches to social science questions.
• Manipulated and analysed large volumes of heterogeneous data or derived machine learning models by taking advantage of parallel, distributed, and other emerging computation methods.
• Selected and potentially extended or retrained machine learning models suitable for social science tasks in classification, interpretation, and explanation of social life.

How to Apply

All applications must be made through the University of Oxford Graduate Admissions site . There are two deadlines for the MSc Programme in November and January. Applications submitted for both deadlines are given equal consideration.

Please ensure that you start the online application process as early as you can, to ensure plenty of time to complete your application. Only applications that are complete by the deadline (including letters of reference) can be considered by the admissions team.

For MSc applicants interested in continuing on to doctoral study, please note that a separate application form to the combined MSc + DPhil (1+3) admissions route is no longer required. To be considered for MSc + DPhil (1+3) funding, applicants should apply to the MSc in Social Data Science only and submit an ESRC Grand Union Doctoral Training Partnership application as part of their other application materials.

This course can also 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.

The Oxford Internet Institute is participating in the University of Oxford’s pilot on selection procedures which aims to explore actions aimed at better contextualising admissions procedures for graduate students while minimising conscious and unconscious bias. For all our courses, the socio-economic data you provide in the application form will be used to contextualise the shortlisting and decision-making processes with a focus on providing opportunities for excellent students regardless of socioeconomic background. For details about the pilot and the actions we are taking, please see the University’s page on the Pilot selection procedure.

• Full-time: 10 months

Start date:

• October 2024

12 noon UK time (midday) on:

• Friday 10 November 2023
• Friday 5  January 2024

Dr Bernie Hogan

Associate Professor, Senior Research Fellow

Bernie is the Programme Director of the MSc course.

David Pepper

MSc Coordinator

David is the MSc Coordinator, and administrates the course.

Our induction programme is usually held in the first week of October, the week preceding the start of Michaelmas Term (also referred to as 0th week). During Induction Week students will be formally introduced to the  OII’s Director, Director of Graduate Studies, Programme Directors, Graduate Studies Support team, as well as our faculty and administrative team.  In addition students will be offered a full tour of the OII’s facilities and introduced to IT and library resources, followed by several informative MSc induction sessions. There is also ample opportunity to get to know fellow students and staff through student-led social activities and an afternoon drinks reception. 

Professional engagement

Over the course of the year the OII generally has a full schedule of lectures which students are welcome to attend, both in person and virtually. These range from formal departmental lectures, bespoke lectures from academic visitors, and Industry Insights lectures featuring discussions about life at a variety of technology-focused organisations, corporations, NGOs, and government departments.

Our MSc students are provided with working space in the department in both the dedicated MSc room at 1 St Giles and additional student working space at 41 St Giles. Students in the MSc program have access to departmental server provisions with both CPU and GPU capabilities as well as opportunities for access to Oxford’s high performance computing resources via Advanced Research Computing (ARC). All students are provided with Office365 and can request additional software provisions such as Overleaf, based on needs. The MSc room is adjacent to the OII’s library, specialising in social sciences, technology and computing. Students also have digital and physical access to the Bodleian Libraries, the University’s main research library.

Pastoral and Welfare Support

In addition to the pastoral support provided your college, as a department the OII seeks to support students by various means. Each degree programme has dedicated administrative support and the administrators in question will be able to help and advise students on a range of matters relating to their studies, or point them towards dedicated sources of support elsewhere in the University. Supervisors and the Director of Graduate Studies can also serve as a source of support, in addition to our dedicated disability lead and several Harassment Officers who can assist with connecting students with the appropriate support.

Social Data Science students take five compulsory foundation courses, three compulsory intensive courses, and two options courses, in addition to their thesis.

Please note that the course offering listed below is provisional, and may be subject to change.

Foundation courses

Social Data Science students take five compulsory foundation courses, designed to provide students with core skills, methods, theories and concepts required to undertake the remainder of the degree. These include laboratory and practical exercises to ensure that students are competent with particular techniques and able to use statistical and other software packages.

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.

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.

Frontiers of Social Data Science

In this course, we 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.

Thesis Seminar

This is a capstone course for students in their final term. It is an opportunity for the whole cohort to reconvene and present their thesis work in progress to the group. Additionally, seminars may include advice on best practices in research and life after the MSc.

Intensive courses

The data science intensive is a series of three course modules taught one after the other in Michaelmas term. The first course, Fundamentals of Social Data Science, is a primer on data science fundamentals in Python, with an emphasis on wrangling, API access, and exploratory data analysis. The second course, Data Analytics at Scale, introduces concepts for efficiency, computability, scalability, and using data on the server. The third course, Introduction to Machine Learning, introduces the basics of classification, neural networks, and various approaches to supervision and learning.

These courses are assessed by a single take home exam which runs from exploratory data analysis to simple predictive models.

Fundamentals of Social Data Science in Python

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. It covers basics of claim-making, analysis, and Python for 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

This course covers the fundamentals of both supervised and unsupervised learning.

Option courses

Each student will select two option courses. The following list is representative, but may be updated.

Applied Machine Learning

This course teaches practical and applied Machine Learning techniques, focusing on how to apply the mathematical foundations of machine learning to domains where we are uncertain about the right answer or best approach, with an emphasis on temporal and graph-based approaches.

Digital Era Government and Politics

This option course will approach the study of government and politics through the lens of data science.

Fairness, Accountability, and Transparency in Machine Learning

Integrating historical and cultural context with contemporary scholarship, this course equips students with the technical and conceptual tools to engage critically with machine learning research and practice.

Internet Economics

A general introduction to the economics of the Internet, and to economics as a tool for social research more generally, emphasising issues such as competition, asymmetric information, trust and privacy, auctions, and network economics.

Introduction to Natural Language Processing for the Social Sciences

This course will develop conceptual and technical tools for large-scale analysis of linguistic data such as document collections, transcripts, and blogs.

Social Network Analysis and Interpretation

An introduction to the analysis of online social networks, providing students with the tools necessary to undertake research on online networks, and to give an overview of the type of questions that these data can answer.

Data-driven Network Science

Data-driven Network Science will introduce the students to network summaries and network models. Then different methods for analysing network data will be presented; these include likelihood-based methods as well as nonparametric methods.

A thesis of approximately 10,000 words (with a maximum of 15,000 words) is the capstone to the MSc experience. It provides 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 on a specialist topic of their choosing.

Academics within the Social Data Science programme will put forward both specific projects as well as general themes in which they would be happy to supervise theses. Students are also encouraged to propose projects of their own. Students will not be required to choose thesis topics until the second term in order to give them ample time for their research interests to develop and the opportunity to discuss topics with relevant faculty members.

Selected Past Alumni Theses

• Sarah Ball (2022) Triple standard in venture financing? The impact of an entrepreneur’s gender on investment decisions in equity crowdfunding
• Conrad Borchers (2022) Stack Overflow Correlation Networks Predict Technology Evolution and Labor Market Relevance
• Matt Chapman (2022) A recipe for gentrification: Predicting urban change with Tripadvisor data and machine learning.A
• Hannah Kirk (2021) Hatemoji – The Construction and Classification of Emoji-based Hate Speech
• Sven Giegerich (2021) Momentum Gains Attention – Enhancing Deep Time Series Momentum Strategies Using Attention-Based Networks
• Clare Brennan (2021) The impact of Ofsted reports on demand for state-funded primary schools in England, from 2012-2016
• Cameron Raymond (2021) Managing Online Rumour Proportions During Offline Protests
• Zo Ahmed (2020) Tackling Racial Bias in Automated Online Hate Detection
• Lisa Oswald (2020) Should we talk to climate skeptics
• Marcel Schliebs (2020) Understanding Social Distribution Networks of Chinese State-Backed Media
• Carla Intal (2019) Dissent and Rebellion in British Parliament

Students are assigned a general supervisor in the first term of their studies. The general supervisor can answer general questions and help the student navigate the department. In the second term students are then assigned a thesis supervisor with more direct topical or research experience for the student’s chosen thesis project. The department provides resources to help students discover faculty and propose a suitable thesis supervisor.

General supervision is provided by faculty from the Oxford Internet Institute. Thesis supervision can additionally span multiple departments, often with co-supervision within the OII. The following faculty members are eligible to supervise MSc Social Data Science students:

Dr Adam Mahdi

Dr Ana Valdivia

Professor Andrew Przybylski

Professor Brent Mittelstadt

Professor Carl-Benedikt Frey

Professor Chris Russell

Professor Ekaterina Hertog

Dr Fabian Braesemann

Dr Fabian Stephany

Dr Gemma Newlands

Professor Gesine Reinert

Dr Grant Blank

Professor Greg Taylor

Professor Helen Margetts

Professor Janet Pierrehumbert

Professor Joss Wright

Professor Kathryn Eccles

Dr Keegan McBride

Dr Luc Rocher

Professor Mark Graham

Professor Min Chen

Professor Philip Howard

Professor Ralph Schroeder

Professor Rebecca Eynon

Professor Renaud Lambiotte

Professor Mariarosaria Taddeo

Professor Sandra Wachter

Dr Scott A. Hale

Dr Varun Kanade

Professor Victoria Nash

Professor Viktor Mayer-Schönberger

Professor Vili Lehdonvirta

Dr Xiaowen Dong

Details of fees, living expenses, and definitions of home and overseas students, together with information about potential sources of funding are available from the  University’s Fees and Funding  website.

There are a number of sources of funding for postgraduate students at Oxford. Details of all scholarships for which candidates may be eligible can be found on the  University’s Fees and Funding  website.  The scholarships are all highly competitive and are awarded on academic merit.

Clarendon Scholarships

Clarendon is one of the biggest of the University’s scholarship schemes, offering around 170 new scholarships each year to academically outstanding graduates. Clarendon scholarships are competitive, prestigious and highly sought-after. As well as providing for fees and living costs Clarendon aims to enhance the Oxford experience by offering students the chance to form lasting social, academic and professional networks. Students can apply by completing the funding sections of the graduate admissions form. As part of the admissions process, the Oxford Internet Institute Scholarship Committee will decide which applicants to nominate to the University for consideration. Further details of this scholarship can be found on the University’s Clarendon Scholarships  page.

ESRC Grand Union Doctoral Training Partnership

The Grand Union DTP ESRC studentship is for MSc applicants who wish to continue on to doctoral study at the OII, or for applicants to the DPhil programme only.

The ESRC is the UK’s largest organisation for funding research on social and economic issues. The University, in collaboration with Brunel University and the Open University, hosts the Grand Union Doctoral Training Partnership – one of fourteen Doctoral Training Partnerships accredited by the ESRC as part of a Doctoral Training Network.

The Oxford Internet Institute’s graduate degree programmes are a recognised doctoral training pathway in the partnership and our Digital Social Science pathway is provided through two routes, Masters-to-DPhil (known as 1+3) and DPhil-only (known as +3), and is available to students studying part-time as well as those studying full-time.

In order to be considered for 1+3 funding via the Grand Union DTP ESRC studentship, you must apply to an OII MSc programme and select ‘ESRC Grand Union DTP Studentships in Social Sciences’ in the University of Oxford scholarships section of the University’s graduate application form. You must also complete a Grand Union DTP Application Form and upload it, together with your graduate application form, to be considered for nomination for the studentship.

Information about ESRC studentships at Oxford can be found on the Grand Union DTP website . Please ensure you have read all of the guidance available on the website before completing the Grand Union DTP Application Form. Questions can be directed to the Grand Union DTP Office .

ESRC studentships are open to both Home (UK) and International candidates, read more about the eligibility criteria here .

Black Academic Futures  

The Black Academic Futures Scholarships offer up to 30 scholarships for UK Black and Mixed-Black students to pursue graduate study at Oxford. Applicants need to apply to an Oxford department by the January programme deadline to be considered for the scholarship and ensure they include the ethnicity information in their application.  

Rhodes and Marshall Scholars

The OII welcomes a number of  Rhodes  and  Marshall Scholars  onto the MSc programme every year. Eligible students should apply for those scholarships before applying for a place on the MSc programme.

Refugee Academic Futures  

The Refugee Academic Futures scheme offers financial support to pursue graduate study at Oxford to students who are refugees or other people with lived experience of displacement. Applicants need to apply to an Oxford department by the January programme deadline to be considered for the scholarship.

Care-Experienced Academic Futures

The Care-Experienced Academic Futures scholarships offer financial support to students who have experienced being in care in the UK to pursue graduate study at Oxford.

Weidenfeld-Hoffmann Scholarships and Leadership Programme  

The Weidenfeld-Hoffmann Scholarships and Leadership Programme provides the opportunity to pursue fully-funded graduate studies at the University of Oxford, combined with a comprehensive programme of leadership development, long-term mentoring and networking.   

To be considered for this scholarship, you must select the Weidenfeld-Hoffmann Scholarships and Leadership Programme in the University of Oxford Scholarships section of the University’s graduate application form and submit your application for graduate study by the January deadline for your course.     

OII Shirley Scholarship

The OII awards a limited number of MSc Scholarships each academic year. These scholarships are open to students (from any country) and all applicants who are offered a place on our programme are automatically considered for an award. Scholarships are awarded on the basis of merit.

Recipients of an OII departmental scholarship will be designated as Shirley Scholars, and they will be supported by the  Shirley Scholars Fund  established in honour of OII founder donor Dame Stephanie Shirley.

MSc Social Data Science – Graduate Handbook

Download the handbook for study in the academic year 2023-2024

You can find general FAQs about applying to our courses, studying at the OII, and choosing a college on the study FAQs page .

How does the MSc in Social Data Science differ from the MSc in the Social Science of the Internet?

The MSc in Social Data Science is designed for students with core quantitative skills who wish to develop their skills for analysing structured and unstructured data using advanced computational techniques such as machine learning. Theses in Social Data Science might develop new computational approaches for analysing human behavioural data and/or apply such approaches to answer a social science question. The MSc in Social Science of the Internet is designed for students interested in research about the Internet and related technologies and their societal implications. Theses in this programme might include quantitative, qualitative, computational or mixed methods applied to a broad range of questions about digital phenomena and could address questions about technology policy or practice.

Should I apply for the MSc or the DPhil in Social Data Science?

A substantial amount of training in our programmes happens at the MSc level. It is therefore expected that applicants to DPhil programmes already hold a taught masters or other advanced degree. For Social Data Science, applicants should examine the MSc Social Data Science courses and are advised to apply for the MSc if their current experience covers less than half of the content taught within the MSc Social Data Science programme. DPhil students will work with their supervisors and the course director to identify any further areas of specialised training that is needed for their theses and opportunities to meet these needs from across the University. DPhil students will usually take the Foundation courses from the MSc Social Data Science unless they already have equivalent training.

Which application deadline should I apply for?

There are two deadlines for the MSc Programme. Applications submitted for both deadlines are given equal consideration, so please choose the deadline that works best for you. Please ensure that you start the online application process as early as you can, to ensure plenty of time to complete your application. Only applications that are complete by the deadline can be considered by the admissions team. All applications must be made through the  University of Oxford Graduate Admissions  site.

If I need to submit English Language Test results, when are they due?

You can read more about the English language requirements for graduate study applications in the graduate application guide.   This course requires proficiency in English at the University’s higher level . If you already have English language test scores at the required level achieved within two years of the start of the course to which you are applying, please include them in your application. However, you are not required to provide test scores when you submit your application.  

How do I choose a supervisor?

Our students are supervised by  OII faculty members and colleagues in partner departments.

Students will be assigned a supervisor in their first term based on their research interests. The supervisor will remain the main point of contact for keeping an eye on academic progress, and will liaise with the student and with other faculty members with whom the student is working with on their thesis.

What fees do I have to pay?

Course fees cover your teaching, and other academic services and facilities provided to support your studies. They do not cover your accommodation or other living costs.

See the University’s  guidance on fee status  and fee liability for information on  Home/Republic of Ireland ,  Islands  and  Overseas  student classification. As well as covering University and College fees, students will also have to support their maintenance costs. As Oxford is a relatively expensive place to live, it is recommended that students consult the University’s  guidance on living costs  when planning their budget, to cover accommodation, meals and other living expenses.

Do I have to live in Oxford during my studies?

Full-time students are required by the University’s regulations to be in residence in Oxford for each of the 8 weeks of Michaelmas and Trinity terms and the 10 weeks of Hilary term. You will be free to leave Oxford after the end of each term but are advised to return during the week prior to the start of the next term (referred to as 0th week).

Do you offer any online or part-time courses?

We do not currently offer any of our MSc or DPhil programmes online, and the MSc in Social Data Science is only offered in a full-time mode due to the intensive nature of several of the core courses. The DPhil in Social Data Science is offered in both full-time and part-time modes, and our MSc in  Social Science of the Internet  is offered part-time.

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The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

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The School of Information offers four degrees:

The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.

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The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.

Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.

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Master of Information and Data Science

Master’s in data science online from uc berkeley.

The number 2–ranked Master of Information and Data Science (MIDS) program,* delivered online, prepares students to be leaders in the data science field.

The online master’s program brings UC Berkeley to students, wherever they are. The WASC-accredited program blends a multidisciplinary curriculum, experienced faculty from UC Berkeley and top data-driven companies, an accomplished network of peers, and the flexibility of online learning.

Request more info

Complete a Rigorous, Holistic Curriculum

The multidisciplinary online data science master’s curriculum draws upon computer science, social sciences, statistics, management, and law. Students use the latest tools and analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems.

The core curriculum focuses on the following key skills:

• Research design
• Data cleansing
• Data engineering
• Data mining and exploring
• Data visualization
• Information ethics and privacy
• Statistical analysis
• Machine learning

Experiential, project-based learning is a hallmark of the MIDS program. Students work collaboratively with the latest tools, environments, and processes on real-world data science problems, so they are prepared to work as leaders in the industry.

Online Learning Experience

The online master’s in data science combines advanced technology and in-person experiences to ensure you benefit from the full UC Berkeley School of Information (I School) experience.

Find all of the online tools you need to succeed in one place: the virtual campus.

• Access your weekly Zoom classes, where you will engage in meaningful discussions.
• Complete coding work through seamless GitHub integration in the virtual campus.
• View upcoming course work and deadlines with one click on your dashboard.
• Attend class or complete course work from anywhere.

Online Data Science Program Paths

The 27-unit online program is designed for the working professional and can be completed at a flexible pace.

The standard path is designed for working professionals to complete part-time and can be completed in 20 months, with two courses per semester.

The accelerated path gives students the opportunity to take three courses per semester to complete the program in only 12 months.

The decelerated path allows students to drop down to one course per semester after the first term and complete the program in no more than 32 months.

Learn more about about upcoming webinars, deadlines, and more

Featured courses.

The MIDS curriculum features a wide range of courses that provide students with a comprehensive understanding of how data science can be used to inform decision-making in their organizations. Students will complete programming-focused courses, like the featured courses below, in concurrence with courses that focus on the ethical impact of data science and how to effectively communicate results.

Applied Machine Learning

Students will learn how to apply crucial machine learning techniques to solve problems, run evaluations and interpret results, and understand scaling up from thousands of data points to billions.

Behind the Data: Humans and Values

This course examines the legal, policy, and ethical issues that arise throughout the full life cycle of data science. Students use case studies to explore these issues across various domains, such as criminal justice, national security, health, marketing, politics, education, and employment.

Natural Language Processing with Deep Learning

This course is a broad introduction to linguistic phenomena and our attempts to analyze them with machine learning. The course covers a wide range of concepts, with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.

Admissions Requirements

The master’s in data science program is seeking applicants who can make a positive impact on the I School community and beyond. A complete application must include the following:

• Online application
• Official transcripts
• Statement of Purpose and additional admissions statements
• Two professional letters of recommendation
• Current resume
• Application fee
• TOEFL scores (if applicable)
• GRE or GMAT scores are optional.

* Best Online Master’s in Data Science Programs in 2023 . (2022). Fortune Education . Retrieved August 22, 2023.

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Your Programmes

University of Oxford

MSc DPhil Social Data Science

1 in 10 applicants to this programme received an offer.

Data shown above is for entry in academic year 2021/22 (sources) .

Previous Years

Why are there inexact numbers? For data protection reasons, when the number of applications, offers, or admissions is low for a given course (or in some cases, regardless of the numbers), some universities report only approximate numbers. Based on these, we have computed the range of possible values.

Data sources

• FOI Request by Albert Warren. December 2019.
• FOI Request by Jiayue Zhao. July 2022.

The acceptance rate , or offer rate, represents the fraction of applicants who received an offer. Note that this will be generally lower the acceptances rates (acceptances divided by applicants) published by many other sources. This article explains it in more detail. The acceptances generally indicate the number of offer holders who accepted the offer and fulfilled its conditions. For some universities, however, it denotes the number of applicants who accepted the offer, regardless of whether they subsequently met its conditions.

Data Reliability

Unless otherwise noted, the data presented comes from the universities and is generally reliable. However, some of the differences between years and/or courses may be due to different counting methodologies or data gathering errors. This may especially be the case if there is a sharp difference from year to year. If the data does not look right, click the "Report" button located near the top of the page.

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