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MSc in Social Data Science

MSc in Social Data Science

Introduction

The multi-disciplinary MSc in Social Data Science provides the social and technical expertise needed to analyse unstructured heterogeneous data about human behaviour, thereby informing our understanding of the human world.  The course is taught by faculty from the Oxford Internet Institute, Engineering Science, Statistics, and other partner departments of the University of Oxford.

The growing field of social data science involves developing the science of these social data: creating viable datasets out of messy, real world data; and developing the tools and techniques to analyse them to tell us something about the world, through explanation, prediction and the testing of interventions. In this way, social data science offers a new science of studying social data combining theories, concepts, and methods from the social sciences and the computational sciences.

Social Data Science will help us understand big issues of crucial interest to the social sciences, industry, and policy-makers including social, economic and political behaviour, interpersonal relationships, market design, group formation, identity, international movement, ethics and responsible ways to enhance the social value of data, and many other topics.

Overview

Social Data Science students take five compulsory foundation papers, three compulsory intensive papers,  two options papers and produce a thesis of up to 15,000 words on topics of the students’ choosing based on discussions with thesis supervisors.

  • Foundation papers cultivate core skills, methods, theories and concepts required for sophisticated study in the field.
  • Intensive papers introduce programming skills for data capture and cleaning, teach statistical and machine learning fundamentals, and cover techniques for scaling analysis to large datasets.
  • Option papers enable students to develop in-depth specialist techniques and disciplinary expertise. With their advisor students can choose the option papers that best fit with their future career path and thesis research from a wide selection spanning the multiple disiciplines of social data science.
  • 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:

  • 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 to answer a social science question.
  • Students looking to transition into research at the intersection of the social sciences and mathematical and computational sciences
  • Students wishing to work in data analytics, business analytics, and other data-intensive roles.

Learning outcomes

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

  • Designed a research project that applies tools and methods from data science to address a social science research question.
  • Evaluated and compared multiple computational approaches to a research question and chosen the most appropriate or efficient one.
  • 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 and become responsive to emerging practices and challenges.
  • Evaluated and compared multiple computational approaches to a scientific challenge and chosen the most appropriate or efficient one.
  • Developed a wide ranging appreciation of both contemporary social and political science theories and data science approaches to tackling research questions related to these theories.
  • Manipulated and analysed large volumes of heterogeneous data to answer social science research questions by taking advantage of parallel, distributed, and other emerging computation methods.
  • Identified the current state-of-the art for analysing large-scale human behavioural data and either innovated with new methods or adapted existing methods to the specific challenges inherent with data related to human behaviour.
  • Applied techniques and tools from software engineering to build robust, reliable, and maintainable tools for analysing, visualising, and modelling data.

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 your personal statement, you must complete this online form, download a PDF of your responses, and upload this to your application on the  University of Oxford Graduate Admissions site.

Applicants to the MSc in Social Data Science programme who plan to continue on to the DPhil programme at the completion of the MSc are encouraged to apply for both programmes by selecting the combined MSc + DPhil Social Data Science (1+3) programme when they apply. Continuation to the DPhil portion of the combined MSc + DPhil programme will require that students meet the normal DPhil admissions requirements and any conditions set to progress to the DPhil in Social Data Science. Applicants for the combined programme should complete the same online form for their personal statement and download their responses as a PDF file. MSc + DPhil applicants should then append their research proposal and the PDF of the online form to their application on the University of Oxford Graduate Admissions website.

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.

Key information

Duration:

  • Full-time: 10 months

Start date:

  • October 2022

Deadlines:

12 noon UK time (midday) on:

  • Friday 17 November 2021
  • Friday 7 January 2022

Student Experience

Radcliffe Camera, Oxford

Induction

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. 

Work space

Our MSc students are provided with working space in the department in our dedicated MSc room, where computing facilities with specialist software are available. We are equipped with advanced video conferencing facilities and high-speed network access. The OII’s library specialises in the social sciences, technology and computing, and our students also have 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 Contact and several Harassment Officers who can assist with connecting students with the appropriate support.

Structure

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

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

Foundation papers

Social Data Science students take five compulsory foundation papers, 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.

Option papers

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

Theses

The thesis of up to 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.

Schedule

Michaelmas Term Hilary Term Trinity Term
Foundations and Frontiers of Social Data Science Foundations and Frontiers of Social Data Science Foundations of Visualisation
Applied Analytical Statistics Research Design for Social Data Science Special Topics in Research Design
Fundamentals of Social Data Science in Python
Option Paper 1
Thesis
Data Analytics at Scale
Option Paper 2
Machine Learning

Supervisors

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.

Supervision for the MSc in Social Data Science spans multiple departments. A supervisor may be found outside the OII, and co-supervision is also possible. The following faculty members are eligible to supervise MSc Social Data Science students:

Professor David Steinsaltz, Department of Statistics, University of Oxford

Dr Adam Mahdi, Department of Engineering Science

Professor Gesine Reinert, Department of Statistics, University of Oxford

Professor Janet Pierrehumbert, Faculty of Linguistics, Philology and Phonetics, University of Oxford

Professor John Coleman, Faculty of Linguistics, Philology and Phonetics, University of Oxford

Professor Michael Osborne, Department of Engineering Science, University of Oxford

Professor Min Chen, Department of Engineering Science, University of Oxford

Dr Pieter Francois, School of Anthropology & Museum Ethnography, University of Oxford

Professor Renaud Lambiotte, Mathematical Institute, University of Oxford

Dr Varun Kanade, Department of Computer Science, University of Oxford

Dr Xiaowen Dong, Department of Engineering Science, University of Oxford

MSc + DPhil

Woman working at a laptop

The DPhil in Social Data Science is designed as a natural continuation of the MSc, and offers the opportunity for students to go deeper into a research topic of their choice. Applicants to the MSc in Social Data Science programme who plan to continue on to the DPhil programme at the completion of the MSc are encouraged to apply for both programmes by selecting the combined MSc + DPhil Social Data Science (1+3) programme when they apply. Continuation to the DPhil portion of the combined MSc + DPhil programme will require that students meet the normal DPhil admissions requirements and any conditions set to progress to the DPhil in Social Data Science.

Students admitted only for the MSc in their first year may later apply to continue on to the DPhil, as may students from other universities who can demonstrate similar preparation at Master’s level elsewhere. Students admitted to the 1+3 programme will be considered for funding for the duration of both degrees. The DPhil is also available on a part-time basis, as some students may wish to spend one year as a full-time student completing the MSc and then switch to part-time to pursue a doctoral degree while working.

Fees & Funding

Fees

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.

Funding

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 140 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 applicants to the combined MSc + DPhil programme (1+3) or 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 a Grand Union DTP ESRC studentship, you must 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, by 12 noon on 22nd January 2021 in order to be considered for nomination for the studentship.

Applicants who wish to be considered for 1+3 funding must indicate in their application an interest in pursuing doctoral work and an interest in ESRC funding; applicants considered for the university competition for DTP funding will be asked to submit a short research proposal.

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.

All applicants must satisfy the ESRC’s citizenship and residence requirements.

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.

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.

FAQs

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?

Applicants who need to fulfil an English Language requirement will be informed of the deadline upon receiving their offer. Please note that if you have taken a test previously, it must be within 2 years of making your application for the results to remain valid, otherwise you will need to retake the test. Applicants are required to provide evidence of proficiency in English at the higher level required at the University. Further details on English language requirements.

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. You may have seen separate figures in the past for tuition fees and college fees. We have now combined these into a single figure.

See the University’s guidance on fee status and fee liability for information on Home/Republic of IrelandIslands 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). In addition students are required to sit written examinations in week 9 of their first term and 0th week and (for certain courses) 10th week in their second term and thus must be resident in Oxford at these times. You will need to submit your thesis in person to the Examination Schools by August 1st (or the nearest working day if this falls at the weekend) and you will also need to be available to return to Oxford in late August or September in the event of being called back for viva voce.

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.