The MSc in Social Data Science provides the ability to collect, manipulate, and analyse large volumes of structured and unstructured data, which is becoming a core social science skill that is in high demand on the job market. The Social Data Science programme focuses on generation and analysis of transactional and other ‘found’ data relating to individual and social behaviour. The course is taught by faculty from the Oxford Internet Institute, Engineering Science, Statistics, and other partner departments of the University of Oxford.

As digital technologies, the Internet, and social media become increasingly integrated into society, our daily lives generate unprecedented quantities of digital data. These data provide opportunities to study complex social systems in frameworks similar to those of the natural sciences with emphasis on empirical observation of patterns in large-scale data, quantitative modelling and experiments to produce and test new theories. This Social Data Science can generate theory-informed predictive models and underpin the way we understand and solve social problems.

Social Data Science will help us understand big issues of crucial interest to the social sciences, industry, and policy-makers including 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.

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.
  • Master’s 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 challenge themselves to understand and overcome the mathematical and computational challenges of analysing structured and unstructured data using machine learning and other techniques.
  • Students wishing to work in data analytics, business analytics, and other data-intensive roles.
  • Students looking to transition into research at the intersection of the social sciences and mathematical and computational sciences.

Learning outcomes

Students completing the MSc in Social Data Science will:

  • Design a research project that applies tools and methods from data science to address a social science research question
  • Evaluate and compare multiple computational approaches to a research question and choose the most appropriate or efficient one
  • Communicate across disciplines and explain research outcomes in an accessible language and to a wide audience
  • Possess a critical understanding of the uses and limitations of current computational approaches to social science questions and be responsive to emerging practices and challenges
  • Evaluate and compare multiple computational approaches to a scientific challenge and choose the most appropriate or efficient one
  • Have a wide ranging appreciation of both contemporary social and political science theories and data science approaches to tackling research questions related to these theories
  • Manipulate and analyse large volumes of heterogeneous data to answer social science research questions by taking advantage of parallel, distributed, and other emerging computation methods
  • Identify the current state-of-the art for analysing large-scale human behavioural data and either innovate with new methods or adapt existing methods to the specific challenges inherent with data related to human behaviour
  • Apply 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. Applications submitted for both deadlines are given equal consideration. Only applications that are complete by the deadline (including letters of reference) can be considered by the admissions team.

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.

  • Foundations and Frontiers of Social Data Science: Providing an intellectual framework for Social Data Science within the landscape of scientific inquiry, including important external issues and challenges that shape the contexts in which Social Data Science takes place and how these influence what social data science is and is not.
  • Applied Analytical Statistics: Focussing on the tools and techniques used by social scientists to understand, describe and analyse (quantitative) data. The focus will be on learning how to apply practical statistics in a social research context (rather than looking at fundamental mathematical foundations of statistical concepts).
  • Research Methods for Social Data Science: Core methods and understandings of data science—reliability, robustness, validity, reproducibility, predictive accuracy—effective research design, and ethical research.
  • Foundations of Visualization: Discussion of the two-way interaction between visualization and the social sciences: (i) using visualization technology in social sciences, and (ii) using social science methodologies to facilitate discourses about visualization.
  • Special topics in Research Design: Cohort-building course with emphasis on research skills development in the run up to the thesis. Students will be grouped into small clusters to work with faculty on shared areas of interest for additional development towards their thesis.

Intensive papers

You are required to take three compulsory intensive papers that introduce the programming skills for data capture and cleaning, teach statistical and machine learning fundamentals, and cover techniques for scaling analysis to large datasets.

  • Python for Social Data Science: Python fundamentals including version control, server access, data clean/wrangling, and APIs
  • Data Analytics at Scale: Discussion of computational complexity, computability, efficiency, and Big-O notation. Tools and frameworks for processing data at scale including MapReduce, Hadoop, and Spark as well as data storage and retrieval techniques (SQL and NoSQL).
  • Machine Learning: Machine learning technologies and applications. Overview of machine learning, optimization, cluster analysis, classification, Gaussian processes, Expectation-Maximization and variational analysis, neural networks and deep learning.

Option papers

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

  • Data Science of Government and Politics: Understanding the study of government and politics through the lens of data science. Students will leave with both a wide ranging grounding in political science and (a) insight into how data science can be used to shed new light on key debates in the field and (b) understanding of where data science is (or could be) changing the political landscape through its use by political actors, such as in large-scale data-led election campaigns; for policy-making; and through the use of algorithms in computational propaganda.
  • Experiments for Data Science: Understanding the experimental method as an essential element of Social Data Science, which offers the potential to explain patterns or irregularities in human behaviour. First, large-scale data may reveal natural experiments where the effect of a change in platform design or commercial offering can be observed in the data ‘as if’ it had been generated randomly. Second, explicit interventions may be made in Randomized Controlled Trials (RCTs), using the internet or mobile platforms as a ‘field’ to explore the potential effect of commercial or policy interventions. Experiments of both kinds may be used to understand social influence and network effects on behavioural outcomes.
  • Human and Data Intelligence: Examining the roles of humans and machines in data intelligence, and the collaboration, cooperation, contention, and competition between them. Bringing together a structured discourse by drawing theories and evidences from mathematics, social science, computer science, and cognitive science. The course encourages students to use social science research methodologies in their comparative analysis of human- and machine-centric processes for data intelligence
  • Introduction to Speech and Language Processing: Introducing a range of computational techniques for the analysis of speech and language, as developed in speech and language technology but here focussed on their use in scientific research.  Covering elements of signal processing, automata theory and parsing.
  • Introduction to Natural Language Processing for the Social Sciences: Developing conceptual and technical tools for large-scale analysis of linguistic data such as document collections, transcripts, and blogs. The first part of the course introduces the statistical structure of the lexicon and models for text creation, including the baseline Naïve Bag of Words model as well as more realistic models that include effects of social and pragmatic context. Then, we turn to algorithms for clustering, classifying, and discriminating different types of documents on the basis of the words and word sequences that they contain. These are applied to characterize the topics of different documents as well as the socio-indexical traits of speakers/authors. Lastly, we bring these ideas together in tools for analysing the spread of memes and opinions through repeated interactions in linguistic communities.
  • Statistical Analysis of Networks: Introduction 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.
  • Sociological Analysis: Developing intellectual skills in explaining social phenomena: identifying puzzles, developing theoretical explanations, and testing them empirically. Each week a lecture introduces one type of explanation, and this is followed by a seminar that discusses empirical research on a related topic. The topic illustrates this type of explanation in practice, revealing its strengths and weaknesses.
  • Sociogenomics: Imparting unique insight into the emerging topic of sociogenomics and the most cutting-edge methodological techniques in this area of research. The focus will be on understanding the key substantive research questions in this area, an overview of UK data that is increasingly available, hands-on computer lesson of how to work with genetic data, and an introduction into the current methodological techniques used in the field.
  • Survival Analysis: Event times and event counts appear in many social and medical data contexts, and require a specialised suite of techniques to handle properly, broadly known as survival analysis. This course covers the basic techniques of estimating event-time distributions, comparing and testing distributions of different populations, and evaluating the goodness of fit of various models. A focus is on understanding when and why particular models ought to be chosen, and on using the standard software tools in R to carry out data analysis.
  • Time Series Econometrics: Analysis of macroeconomic data using time series methods. A key feature of time series data is the temporal dependence of the observations, which can often be captured by linear univariate and multivariate autoregressive models. Such autoregressive models will be analysed in terms of econometric properties, interpretation, asymptotic distribution theory as well as empirical illustrations. The first part of the lectures will cover analysis of stable, or stationary, autoregressions. Since most macroeconomic time series appear non-stationary, the second part will cover the analysis of non-stationary autoregressions and cointegration.

Timetable

The MSc course runs from October to August. Oxford University terms are referred to as Michaelmas Term, Hilary Term, and Trinity Term and normally last eight weeks. Please note that this information is provisional, and may be subject to change.

Michaelmas Term Hilary Term Trinity Term
Foundations and Frontiers of Social Data Science Foundations and Frontiers of Social Data Science Foundations of Visualization
Applied Analytical Statistics

 

Research Methods for Social Data Science Special topics in Research Design
Python for Social Data Science Option Paper 1  Thesis
Data Analytics at Scale Option Paper 2
Machine Learning

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.

Faculty members from the OII, as well as faculty from other University of Oxford departments, are available to supervise MSc Social Data Science students:

Dr Scott Hale, Oxford Internet Institute (social data science, human factors in computing, natural language processing, bilingualism). Course Director of the Social Data Science programme and course instructor for Foundations of Visualisation, Special Topics in Research Design, Data Analytics at Scale, Machine Learning.

Professor Gesine Reinert, Department of Statistics (statistical analysis of networks, models for networks). Course instructor for Statistical Analysis of Networks.

Professor Donna Kurtz, Oxford e-Research Centre, Department of Engineering Science (digital technologies, linked data, global heritage applications).

Dr Grant Blank, Oxford Internet Institute (inequality, attitudes, political opinion formation).

Dr Joss Wright, Oxford Internet Institute (censorship, privacy, surveillance, network anomalies).

Dr Janet B. Pierrehumbert, Oxford e-Research Centre, Department of Engineering Science (natural language processing, text mining, social networks). Course instructor for Introduction to Natural Language Processing for the Social Sciences.

Dr Jonathan Bright, Oxford Internet Institute (digital politics, smart cities, elections, news). Course instructor for Data Science of Politics and Government and Applied Analytical Statistics.

Professor David Steinsaltz, Department of Statistics (demography, ageing, genomics, stochastic modelling, longitudinal data). Course instructor for Survival Analysis.

Dr Bernie Hogan, Oxford Internet Institute (social networks, identity, algorithms, real names). Course instructor for Python for Social Data Science.

Professor Ralph Schroeder, Oxford Internet Institute (social issues in big data). Course instructor for Foundations of Social Data Science.

Dr Pieter Francois, Department of Anthropology and Museum Ethnography (digital humanities, historical and archaeological big data, text mining, ritual and religion).

Dr Taha Yasseri, Oxford Internet Institute (computational social science, network science, agent-based modelling, human dynamics, collective behaviour).

Professor Min Chen, Oxford e-Research Centre, Department of Engineering Science (data visualisation, data analysis, empirical studies, social science methodologies in visualisation, theories of visualisation). Course instructor for Human and Data Intelligence.

Professor Vili Lehdonvirta, Oxford Internet Institute (digital economies, labour markets, reputation).

Dr Renaud Lambiotte, Mathematical Institute (network science, dynamics on networks, urban systems).

Professor John Coleman, Faculty of Linguistics, Philology, and Phonetics (speech, language). Course instructor for Introduction to Speech and Language Processing.

Dr Xiaowen Dong, Department of Engineering Science (social influence, human dynamics, urban computing).

Course 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 webpages.

Funding

Clarendon Scholarships

ESRC Grand Union Doctoral Training Partnership

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 25th January 2019 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’.

Other scholarships and funding

Further information on scholarships and funding is available on the University’s Graduate Admissions website.

The DPhil in Social Data Science starting in 2019 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 programme who plan to continue on to the DPhil programme at the completion of the MSc are encouraged to apply for both programmes as part of their application by selecting the MSc+DPhil (1+3) Social Data Science 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 the master’s level elsewhere. All students will have to complete their master’s, normally with a mark of at least 67%, and will be interviewed about their research proposals in order to progress to the DPhil programme. Students admitted to the 1+3 programme will be considered for funding for the duration of both degrees. The DPhil will also be 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.

About the MSc Programme

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 Social Data Science MSc courses and are advised to apply for the MSc if their current experience covers less than half of the content taught within the Social Data Science MSc 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 Social Data Science MSc 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.

What about Brexit?

The University of Oxford is an international institution with students from across the world. Current University policy is that students from the EU applying for this programme this year will only be liable for Home/EU fees for the duration of the course. Further up-to-date details are on the University’s page on the consequences of the EU referendum.

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.

Is the 2000 word limit on the written work in applications a minimum or maximum?

2000 words is a maximum. Many students who find that their best work exceeds this length choose to submit a 2000 word extract from that longer piece of work. We recommend that your chosen piece: demonstrates your capacity for independent or original thought; is systematically analytical rather than purely descriptive; addresses a clear question or problem; where relevant, draws on data or literature sources to support its main arguments; and expresses its arguments with clarity and precision.

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.

Where can I find out about scholarships?

Information on scholarships and funding is available at: http://www.ox.ac.uk/admissions/graduate/fees-and-funding

How many of my references have to be academic? Can I submit references that are not academic?

Of the three required references, at least one should be academic. You are welcome to submit professional references as well, as long as they are able to comment on your academic potential.

What do I do about references if I have been out of academia for a few years?

The OII actively encourages applications from those with valuable experience in the private and public sectors and those who have interrupted their studies for other reasons. We judge every application in a holistic manner on its individual merits and the main role of the admissions process is to assess candidates’ academic potential and intellectual suitability for graduate study. With this in mind, mid-career applicants are encouraged to select or produce written work that demonstrates their ability for independent analytical thought. Non-academic referees are encouraged to comment, in particular, on candidates’ intellectual capacity and analytical skills.

Do you offer any online or part-time courses?

We do not currently offer any of our courses online, and the Social Data Science MSc is only offered in a full time mode currently. The Social Data Science DPhil (starting 2019) will be offered in both fulltime and part-time modes. Our Social Science of the Internet MSc is offered part-time. The part-time mode of study is not suitable to non-EU students who do not already live in the UK before the course begins, as student visas are not issued for part-time study at the University of Oxford. For further information, please refer to https://www.gov.uk/browse/visas-immigration.

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 living costs below). 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 / EU / 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 budget between £10,000 and £12,500 per annum to cover accommodation, meals and other living expenses.

Why do I need to choose a college?

Oxford is a collegiate university: students and teaching staff belong both to a department and to a college. Colleges typically provide library and IT facilities, accommodation, welfare support, and sports and social events. Graduate students also benefit from the Middle Common Room (MCR) in their college – both a physical space and an organisation, it provides social events, advice, and a link to the graduate community. Your college will have a Tutor for Graduates or Senior Tutor whose role includes general oversight of all graduate members of the college, although your academic studies will be directed by your department or faculty. Each graduate student has a college adviser, a senior member of the college’s staff who will be able to offer support and advice. Further information is available on choosing a college on the University website, and from college prospectuses.

How do I decide on which college to choose?

We can’t advise applicants on their choice of college, however, all teaching is organised within the department so college choice will not make any http://www.ox.ac.uk/admissions/graduate/courses/msc-social-science-internetsignificant difference to the way that students are taught or supervised. When making your choice, first check which colleges accept applications from OII students, then check the individual college websites. Factors you should consider when making your choice include location, accommodation quality (and your eligibility for this), library facilities, any financial support the college may be able to offer (e.g. awards, bursaries or scholarships) and the collegiate atmosphere. Note that some colleges accept only graduate students or mature students. If you select a particular college as a preference it does not mean that you will be automatically offered a place there.

If I am accepted on a Programme, am I guaranteed a place at a college?

Yes: Once you have received an offer from the department, your application will go forward for consideration by your preferred college, or the Graduate Admissions and Funding team will assign you a college for consideration if you have not selected a college preference. In the event of heavy over-subscription of a particular college, you may be allocated a place at another college. Colleges will contact candidates separately with their offer, subject to satisfaction of any funding conditions. A college decision can take 8-10 weeks following the departmental decision. The University does not guarantee accommodation at a college for its graduate students. However, many colleges do attempt to provide accommodation for graduate students during their first year of study, particularly in the case of international students. If your college is unable to provide any accommodation or the type of accommodation you need, you can contact the University Accommodation Office for further information and assistance.

I’m an international student!

The University of Oxford has a long tradition of welcoming international students, and currently 63% of all graduate students are from outside the UK. We recommend that you consult the University’s International Office, which provides information to support international applications, such as on immigration and Visas, scholarships and funding, US Graduate Student Loans, English Language requirements, Orientation Programmes, etc. (EU students may also wish to consult the University’s page on the consequences of the EU referendum.)

What provisions are there for students with disabilities?

The University of Oxford is committed to providing equality of opportunity and improving access for all people with disabilities who work and study at the University. The University Disability Office has information about the support offered to help those with a disability maintain their track record of academic success as they pursue their studies. The ground floor of the OII is wheelchair-accessible, providing access to the library, seminar room, student common room and disabled toilet.

What facilities does the OII offer its students?

Our MSc students are provided with working space in the department. We are equipped with advanced video conferencing facilities and high-speed network access. Our library specialises in the social sciences, technology and computing, and our students also have access to the Bodleian Library, the University’s main research library. Students are encouraged to engage fully in the intellectual life of the department, e.g. through participation in workshops, departmental seminars, and research projects.

Do I have to live in Oxford during my studies?

You are required by the University’s regulations to be in residence in Oxford for each of the 8 weeks of Michaelmas, Hilary, and Trinity terms. 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 0th week of their second term and thus must be resident in Oxford at this time.  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.

 

This page was last modified on 21 September 2018