phd data science online uk

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Data Science, Technology and Innovation (Online Learning) MSc, PgDip (ICL), PgCert (ICL), PgProfDev

Awards: MSc, PgDip (ICL), PgCert (ICL), PgProfDev

Study modes: Part-time Intermittent Study, Full-time

Online learning

Funding opportunities

Programme website: Data Science, Technology and Innovation (Online Learning)

Online Learning Essentials

Join us on 27th March to learn more about studying an online Masters degree at Edinburgh

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Programme description

Demand is growing for high value data specialists across the sciences, medicine, arts and humanities. The aim of this unique, modular, online distance learning programme is to enhance existing career paths with an additional dimension in data science.

The programme is designed to fully equip tomorrow’s data professionals, offering different entry points into the world of data science – across the sciences, medicine, arts and humanities.

Students will develop a strong knowledge foundation of specific disciplines as well as direction in technology, concentrating on the practical application of data research in the real world.

Our online learning technology is fully interactive, award-winning and enables you to communicate with our highly qualified teaching staff from the comfort of your own home or workplace.

Our online students not only have access to the University of Edinburgh’s excellent resources, but also become part of a supportive online community, bringing together students and tutors from around the world.

Studying online at Edinburgh

Find out more about the benefits and practicalities of studying for an online degree:

• Postgraduate online learning

Programme structure

You can study to the following levels:

• Postgraduate Diploma
• Postgraduate Certificate
• Postgraduate Professional Development (PPD)

PPD credits will be recognised in their own right for postgraduate level credits or may be put towards gaining a higher award such as a PgCert, Diploma or MSc.

Find out more about compulsory and optional courses

We link to the latest information available. Please note that this may be for a previous academic year and should be considered indicative.

Learning outcomes

The modular course structure offers broad engagement at different career stages. Individual courses provide an understanding of modern data-intensive approaches while the programme provides the knowledge base to develop a career that majors in data science in an applied domain.

Career opportunities

This programme is intended for professionals wishing to develop an awareness of applications and implications of data intensive systems. Our aim is to enhance existing career paths with an additional dimension in data science, through new technological skills and/or better ability to engage with data in target domains of application.

Introduction to the DSTI Programme

Prof dave robertson (head of cse), introduction to data science, technology and innovation programme, dr areti manataki (senior researcher in the school of informatics), introduction to medical informatics course, entry requirements.

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 1 Oct 2024.

The programme is designed to be accessible. We welcome applicants who meet the standard academic entrance requirements and those with relevant work experience.

A UK 2:1 honours degree, or its international equivalent.

We will also consider a UK 2:2 honours degree, or its international equivalent, in Computer Science, Informatics, Software Engineering, Computational Physics, Mathematical Physics, Mathematics, Statistics, Computational Chemistry, Chemistry with Computer Science, Physics with Computer Science, or Computational Biology.

All applicants need to have some understanding of basic computer programming concepts. If your undergraduate degree discipline is not listed above, you must highlight on your application any relevant knowledge/experience.

We will also consider your application if you have relevant work experience. If you plan to apply on this basis, please include a detailed CV and outline how your professional background demonstrates your ability to undertake the programme in the Relevant Knowledge/Training section of your application. If you are unsure if you have relevant work experience, please email the Data Science team. You may be admitted to the Postgraduate Professional Development route in the first instance.

We strongly recommend that all applicants have SQA Higher or GCE A level Mathematics, or equivalent, and ideally some mathematics classes taken at undergraduate level. We also recommend that students have some experience of computer programming (e.g. C, Fortran, Java, Python, R).

• Email the Data Science team

Students from China

This degree is Band C.

• Postgraduate entry requirements for students from China

International qualifications

Check whether your international qualifications meet our general entry requirements:

• Entry requirements by country
• English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

• IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
• TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
• C1 Advanced ( CAE ) / C2 Proficiency ( CPE ): total 176 with at least 169 in each component.
• Trinity ISE : ISE II with distinctions in all four components.
• PTE Academic: total 62 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS , TOEFL, Trinity ISE or PTE , in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

• UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

• Approved universities in non-MESC

If you are not a national of a majority English speaking country, then your degree must be no more than five years old* at the beginning of your programme of study. (*Revised 05 March 2024 to extend degree validity to five years.)

Find out more about our language requirements:

Fees and costs

Details can be found in the course descriptors within the programme codes listed above in Programme Structure.

Tuition fees

Scholarships and funding, featured funding.

• Online Learning Scholarships

Search for scholarships and funding opportunities:

• Search for funding

Further information

• College Admissions
• Phone: +44 (0)131 650 5737
• Contact: [email protected]
• Bayes Centre
• The University of Edinburgh
• 47 Potterrow
• Central Campus
• Programme: Data Science, Technology and Innovation (Online Learning)
• School: Informatics
• College: Science & Engineering

Select your programme and preferred start date to begin your application.

MSc Data Science, Technology and Innovation (ICL) - 6 Years (Part-time Intermittent Study)

Msc data science, technology and innovation - 1 year (full-time), pgdip data science, technology and innovation (icl) - 4 years (part-time intermittent study), pgcert data science, technology and innovation (icl) - 2 years (part-time intermittent study), pg professional development in data science, technology and innovation (icl) - 2 years (part-time intermittent study), application deadlines.

You must apply at least one month prior to the start date of the programme so that we have enough time to process your application. If you are also applying for funding then we strongly recommend you apply as early as possible.

• How to apply

You must submit one reference with your application.

Find out more about the general application process for postgraduate programmes:

Imperial College London Imperial College London

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• Data Science Institute

The DSI hosts a number of PhD students, funded from a variety of mechanisms including industry, research funders and self-funded. All applications for a PhD programme need to be submitted through the department where the chosen supervisor sits. For example, if the supervisor is hosted in the Department of Computing, visit  this page with relevant information about the application process.

The DSI are currently advertising for a PhD studentship in collaboration with the China State Shipbuilding Corporation (CSSC) and Jiangsu Automation Research Institute (JARI) to produce the next generation of Data Scientists, if you are interested you can find further information on our vacancy page . The closing date for applicants is 28th February 2021. 

Imperial College London received funding from UKRI for a Centre for Doctoral Training in  AI for Healthcare  which is currently open for applications. More information on the CDT can be found  here .

Axel Oehmichen

"This dual position as a researcher and a student has proven extremely rich in experiences as I was learning and collaborating with other DSI researchers across different fields."

Dr Axel Oehmichen

Axel on his time at the DSI; "I was a part-time PhD student and a research associate working on the eTRIKS and OPAL projects. My research focused on the development of a new platform called the eTRIKS Analytical Environment (eAE) as an answer to the needs of analysing and exploring massive amounts of medical data in a privacy preserving fashion. This dual position as a researcher and a student has proven an extremely enriching experiences as I was learning and collaborating with other DSI researchers across different fields. Those collaborations have brought me new perspectives, allowed me to explore new fields and helped me grow as a researcher. I am an engineer by training and, while it was sometimes challenging, that duality made it possible to join both worlds during my PhD and facilitated my transition to the start-up world". 

Hao Dong  

Akara Supratak Akara Supratak was a PhD student at the Data Science Institute (DSI) from 2013 to 2017, supervised by Professor Yike Guo. During his PhD, he has developed a deep learning model, named DeepSleepNet, for automatic sleep stage scoring, which can achieve state-of-the-art performance ( https://github.com/akaraspt/deepsleepnet ). The study at DSI has given him an opportunity to learn and work with other researchers across different fields such as distributed computing and health informatics, and has broadened his knowledge and experience in doing frontier research.

What is he doing now : He is an instructor at the Faculty of Information and Communication Technology (ICT), Mahidol University, Thailand. Currently, he teaches several courses for undergraduate students such as Fundamentals of Programming and Computer Architecture. His research focuses on Machine Learning, Biosignal Processing, and Image Processing.

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LSE PhD Studentship in Data Science

For 2023 entry, LSE is offering a doctoral studentship for PhD study affiliated to the Data Science Institute (DSI). 

Applications are welcome from both students applying to core data science programmes (Statistics, Mathematics, or Methodology) as well as from applied departments across the School, as long as their projects involve data science or computational social science methods.

The successful student will join a growing cohort of existing DSI-hosted PhD students as well as a regular stream of visiting PhD students in data science. 

Eligibility

Selection for this studentship is on the basis of outstanding academic merit and research potential. This relates both to your past academic record and to an assessment of your likely aptitude to complete a PhD in your chosen topic in the time allocated.

Scholarship amount

The LSE Data Science PhD Studentship is tenable for four years and covers full fees along with an annual stipend of £19,668 (2022/23 rate).

How to apply

To be considered, you must submit a complete application (including references, proposal, marked work etc) by the funding deadline below.  

• funding deadline for all LSE PhD Studentships for 2023 entry: 13 January 2023

For more information visit  how to apply  for a place on a PhD programme.

Fees and funding Scholarships, studentships, loans and tuition fees

How to apply The application process, UCAS and when to apply

Undergraduate fees and funding Details on available scholarships, bursaries, loans and tuition fees

Graduate fees and funding Details on available scholarships, bursaries, loans and tuition fees

Contact us Get in touch with the Financial Support Office

Meet, visit and discover LSE Webinars, videos, on campus events and visits around the world

DPhil in Social Data Science

• Entry requirements
• Funding and Costs

College preference

• How to Apply

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.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Oxford Internet Institute and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff.

Supervision for the DPhil in Social Data Science spans multiple departments (please see the full list of faculty members  eligible to supervise DPhil students for this programme). A supervisor may be found outside the list on the course web page, and co-supervision is also possible. All students will have at least one supervisor who is a faculty member of the OII.

Students should normally expect to meet with their supervisor at least three to four times a term. A more typical pattern is weekly or bimonthly, at least until you reach the stage of writing up your thesis.

The first year is a probationary year, soon after which, subject to satisfactory progress, you will be expected to transfer from Probationer Research Student (PRS) status to full DPhil status. The Transfer of Status takes place within a maximum of four terms for full-time students or eight terms for part-time students. A second formal assessment of progress, Confirmation of Status, takes place later in the programme, normally at the end of the third year. The Transfer of Status and Confirmation of Status assessments are conducted by two members of staff other than the student’s supervisor(s) or advisors.

The sequence of milestones for a DPhil student are as follows:

• Admission as a Probationer Research Student (PRS)
• Transfer to DPhil status (‘Transfer of Status’)
• Confirmation of DPhil status for DPhil students (‘Confirmation of Status’)
• Submission of thesis

Students initially admitted to the status of Probationer Research Student (PRS) are required to attend and pass core modules from the OII’s training programme. Students who have already completed similar courses in their past academic career should request an exemption from one or more modules by providing sufficient evidence.  

A successful transfer of status from PRS to DPhil status will require the student to show that their proposed thesis represents a viable topic and that their written work and interview show that they have a good knowledge and understanding of the subject. Students are also required to demonstrate satisfactory completion of the foundational courses by this point.

Following successful transfer, students will need to apply for and gain confirmation of DPhil status to show that the work continues to be on track. This will need to be completed within nine terms of admission for full-time students and 18 terms of admission for part-time students.

Both milestones involve an interview with two assessors (other than your supervisor) and therefore provide important experience for the final oral examination.

Full-time students will be expected to submit an original thesis of not more than 100,000 words three or, at most, four years from the date of admission. If you are studying part-time, you be required to submit your thesis after six or, at most, eight years from the date of admission. To be successfully awarded a DPhil in Social Data Science you will need to defend your thesis orally (viva voce) in front of two appointed examiners.

Graduate destinations

The Oxford Internet Institute provides you with skills and opportunities in teaching, research, policymaking and business innovation. Employers recognise the value of a degree from the University of Oxford, and the OII’s doctoral students regularly go on to secure excellent positions in industry, government, and NGOs. 

Alumni who have pursued academic careers have taken up research and teaching positions including notably at the University of Oxford, Cornell University, University of Hong Kong, Imperial College London, and TU Delft. OII DPhil alumni have worked in a wide range of organisations including The World Bank, Open Technology Fund, Oxfam, Cisco, McKinsey, and Google.

The OII Alumni page  features interviews from both MSc and DPhil alumni about their time at the Department and career paths after Oxford.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.

For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2024-25

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. 

Degree-level qualifications

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.

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.

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other qualifications, evidence of excellence and relevant experience

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.

Applicants are expected to demonstrate quantitative aptitude or experience in at least half of the material covered by the MSc in Social Data Science.

Applicants may demonstrate this aptitude/experience in a variety of ways including:

• graduate and undergraduate transcripts;
• on-the-job training and practical experience;
• evidence of the successful completion of online courses.

Applicants are not expected to have published academic work previously, although publication may help the assessors judge your writing ability and thus could help your application.

Academic research related to data science or experience working in related businesses is not required, but may be an advantage.

Part-time applicants will also be expected to demonstrate their ability to commit sufficient time to study and spend a minimum of 30 days in Oxford per year, including attendance of teaching, seminars and departmental events, to complete coursework, and attend course and University events and modules. If applicable, evidence should also be provided of the employer’s commitment to make time available for study, and of the student’s permission to use employers’ data in the proposed research project.

English language proficiency

This course requires proficiency in English at the University's  higher level . If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's higher level are detailed in the table below.

*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE) † Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)

Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides  further information about the English language test requirement .

Declaring extenuating circumstances

If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.

You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The  How to apply  section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Supporting documents

You will be required to supply supporting documents with your application. The  How to apply  section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.

Performance at interview

Interviews are held as part of the admissions process.

All applications are reviewed by at least two members of faculty with relevant experience and expertise. Applicants are shortlisted based on the quality of the written application. Those who are shortlisted will usually be interviewed.

Interviews are typically held three to six weeks after the application deadline. There is usually only one interview held, which lasts 30 to 40 minutes and can be held via a video conferencing platform. You will be asked questions about your academic background, your research plan, and why you think the Oxford Internet Institute would be the best place to conduct your studies. The interview panel will consist of at least two interviewers which will normally include the potential supervisor.

How your application is assessed

Your application will be assessed purely on your proven and potential academic excellence and other entry requirements described under that heading.

References  and  supporting documents  submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process. Whether or not you have secured funding will not be taken into consideration when your application is assessed.

An overview of the shortlisting and selection process is provided below. Our ' After you apply ' pages provide  more information about how applications are assessed . 

Shortlisting and selection

Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:

• socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of  the University’s pilot selection procedure  and for  scholarships aimed at under-represented groups ;
• country of ordinary residence may be taken into account in the awarding of certain scholarships; and
• protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.

Initiatives to improve access to graduate study

This course is taking part in 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, socio-economic data (where it has been provided in the application form) will be used to contextualise applications at the different stages of the selection process.  Further information about how we use your socio-economic data  can be found in our page about initiatives to improve access to graduate study.

Processing your data for shortlisting and selection

Information about  processing special category data for the purposes of positive action  and  using your data to assess your eligibility for funding , can be found in our Postgraduate Applicant Privacy Policy.

Admissions panels and assessors

All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).

Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.

Other factors governing whether places can be offered

The following factors will also govern whether candidates can be offered places:

• the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the  About  section of this page;
• the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
• minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.

Offer conditions for successful applications

If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our ' After you apply ' pages provide more information about offers and conditions . 

In addition to any academic conditions which are set, you will also be required to meet the following requirements:

Financial Declaration

If you are offered a place, you will be required to complete a  Financial Declaration  in order to meet your financial condition of admission.

Disclosure of criminal convictions

In accordance with the University’s obligations towards students and staff, we will ask you to declare any  relevant, unspent criminal convictions  before you can take up a place at Oxford.

Academic Technology Approval Scheme (ATAS)

Some postgraduate research students in science, engineering and technology subjects will need an Academic Technology Approval Scheme (ATAS) certificate prior to applying for a  Student visa (under the Student Route) . For some courses, the requirement to apply for an ATAS certificate may depend on your research area.

The DPhil in Social Data Science is offered by the Oxford Internet Institute (OII) in partnership with Statistics, Engineering Science, Sociology, and other departments. The OII faculty works at the cutting-edge of their fields, and this innovative research is fully reflected in their course teaching. The department prides itself on providing a stimulating and supportive environment in which all students can flourish. As a fully multidisciplinary department, the OII offers you the opportunity to study academic, practical and policy-related issues that can only be understood by drawing on contributions from across many different fields.

In addition to the formal requirements of the DPhil thesis, all OII doctoral students have access to regular training in the key professional skills necessary to support their research and future employment. These range from classes on advanced research methods as part of the OII’s option course offerings, to professional development training (provided both by the department and the University) such as presentation skills, academic writing and navigating the process of peer review.

You will attend a weekly seminar in which you will present your own work for critique, and critique the work of your peers. The OII also provides opportunities for DPhil students to gain teaching experience through mentored assistantship roles in some of its core MSc courses.

The department's busy calendar of seminars and events brings many of the most important people in internet research, innovation and policy to the OII, allowing students to engage with cutting-edge scholarship and debates around the internet and digital technologies.

OII students also take full advantage of the substantial resources available at the University of Oxford, including world-leading research facilities and libraries, and a buzzing student scene. The departmental library provides students access to a range of resources including the texts required for the degree. Other University libraries provide valuable additional resources of which many students choose to take advantage.

Oxford Internet Institute

The Oxford Internet Institute (OII) is a dynamic and innovative department for research and teaching relating to the internet, located in a world-leading traditional research university. The multidisciplinary OII offers the opportunity to study academic, practical and policy-related issues that can only be understood by drawing on contributions from many different fields.

The OII is the only major department in a top-ranked international university to offer multidisciplinary courses in the social sciences dedicated to understanding the impact of the internet, data, and information technologies on society. We offer masters and doctoral level education across several degrees focused on social data science or the social science of the internet and technology.

Digital connections are now embedded in almost every aspect of our daily lives, and research on individual and collective behaviour online is crucial to understanding our social, economic and political world. As a fully multi-disciplinary department, we offer our students the opportunity to study academic, practical and policy-related issues and pursue cutting-edge research into the societal implications of the internet and digital technologies.

Our academic faculty and graduate students are drawn from many different disciplines: we believe this combined approach is essential to tackle society’s big questions. Together, we aim to positively shape the development of our digital world for the public good.

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The University expects to be able to offer over 1,000 full or partial graduate scholarships across the collegiate University in 2024-25. You will be automatically considered for the majority of Oxford scholarships , if you fulfil the eligibility criteria and submit your graduate application by the relevant December or January deadline. Most scholarships are awarded on the basis of academic merit and/or potential. 

For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.

Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:

Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.

Further information about funding opportunities for this course can be found on the institute's website.

Annual fees for entry in 2024-25

Full-time study.

Further details about fee status eligibility can be found on the fee status webpage.

Information about course fees

Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges .

Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.

Continuation charges

Following the period of fee liability , you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

Where can I find further information about fees?

The Fees and Funding  section of this website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Additional information

There are no compulsory elements of this programme that entail additional costs beyond fees and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Please note that you are required to attend in Oxford for a minimum of 30 days each year, and you may incur additional travel and accommodation expenses for this. Also, depending on your choice of research topic and the research required to complete it, you may incur further additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Whilst many graduate students do undertake employment to support their studies, please remember that students on the full-time arrangement of the OII's DPhil programme are subject to limits on the number of hours that may be worked each week. Part-time student are not subject to these limitations.

Within these limitations, many of the OII's existing full-time DPhil students have been employed on a short or long-term basis as Research Assistants on grant-funded projects gaining valuable research experience. The OII also offers Teaching Assistant positions on the MSc degree for DPhil students who can display the appropriate skills. In addition, there are employment opportunities within the University (such as teaching, translation, and research assistance) as well as within the OII.

For full information on employment whilst on course, please see the University's  paid work guidelines for Oxford graduate students .

Living costs

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

For the 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.

If you are studying part-time your living costs may vary depending on your personal circumstances but you must still ensure that you will have sufficient funding to meet these costs for the duration of your course.

Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs). 

If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief  introduction to the college system at Oxford  and our  advice about expressing a college preference . For some courses, the department may have provided some additional advice below to help you decide.

The following colleges accept students for full-time study on this course:

• Blackfriars
• Campion Hall
• Christ Church
• Exeter College
• Green Templeton College
• Hertford College
• Jesus College
• Keble College
• Kellogg College
• Linacre College
• Nuffield College
• Reuben College
• St Antony's College
• St Catherine's College
• St Cross College
• St Hilda's College
• Wadham College
• Wolfson College
• Wycliffe Hall

The following colleges accept students for part-time study on this course:

Before you apply

Our  guide to getting started  provides general advice on how to prepare for and start your application. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance . Check the deadlines on this page and the  information about deadlines  in our Application Guide.

Application fee waivers

An application fee of £75 is payable per course application. Application fee waivers are available for the following applicants who meet the eligibility criteria:

• applicants from low-income countries;
• refugees and displaced persons; 
• UK applicants from low-income backgrounds; and 
• applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.

You are encouraged to  check whether you're eligible for an application fee waiver  before you apply.

Readmission for current Oxford graduate taught students

If you're currently studying for an Oxford graduate taught course and apply to this course with no break in your studies, you may be eligible to apply to this course as a readmission applicant. The application fee will be waived for an eligible application of this type. Check whether you're eligible to apply for readmission .

Do I need to contact anyone before I apply?

You are recommended to contact a potential supervisor (or supervisors) in the first instance to get feedback on the fit of your proposed research with the expertise of the supervisor before you apply. The full list of faculty members eligible to supervise DPhil students for this course, including their research interests and contact details, can be found on the departmental website. Please note that the Oxford Internet Institute will only admit students where appropriate supervision is available.

Completing your application

You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents .

For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application .

If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.

Proposed field and title of research project

Under the 'Field and title of research project' please enter your proposed field or area of research if this is known. If the department has advertised a specific research project that you would like to be considered for, please enter the project title here instead.

You should not use this field to type out a full research proposal. You will be able to upload your research supporting materials separately if they are required (as described below).

Proposed supervisor

If known, under 'Proposed supervisor name' enter the name of the academic(s) who you would like to supervise your research. Otherwise, leave this field blank.

Referees: Three overall, academic and/or professional

Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Professional references are acceptable, particularly if you have been out of education for some time, but should focus particularly on your intellectual abilities rather than more narrowly on job performance.

Your references will be assessed for:

• your intellectual ability;
• your academic achievement; and 
• your motivation and interest in the course and subject area.

Official transcript(s)

Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.

More information about the transcript requirement is available in the Application Guide.

Personal statement and research proposal: Statement of a maximum of 500 words and a proposal of a maximum of 2,500 words

Your statement of purpose/personal statement and research proposal should be submitted as a single, combined document with clear subheadings. Please ensure that the word counts for each section are clearly visible in the document.

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.

Your statement should be written in English and be a maximum of 500 words.

If possible, please ensure that the word count is clearly displayed on the document.

Your statement will be assessed for:

• interest and commitment for the study of social data science;
• evidence of aptitude for working with data-driven research; and
• alignment of your areas of interest with the availability of supervision, as all students will be assigned a supervisor to guide their research.

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.

The proposal should be submitted in English only and be a maximum of 2,500 words. The word count does not need to include any bibliography or brief footnotes.

Your research proposal will be assessed for:

• the coherence of your proposal;
• the relevance of the topic as it relates to the research of the Oxford Internet Institute and collaborating department;
• the clarity of research question(s), and the knowledge gap the proposal intends to fill;
• the appropriateness of the methods and research design as related to the research question(s); and
• the overall quality of the project proposed.

It is normal for your ideas to change in some ways as you commence your research and develop your project. However, you should make the best effort you can to demonstrate the extent of your research question, sources and method at this moment.

Written work: One essay of a maximum of 2,000 words

An academic essay or other writing sample from your most recent qualification, written in English, is required. If you have not previously written on areas closely related to the proposed research topic, you may provide written work on any topic that best demonstrates your academic abilities. The written work does not need to be data science related, but should demonstrate your critical and analytical capabilities and ability to present ideas clearly. 

The word count does not need to include any bibliography or brief footnotes. Extracts of the required length that originally come from longer essays are also acceptable.

This will be assessed for:

• a comprehensive understanding of the subject area, including problems and developments in the subject;
• your ability to construct and defend an argument;
• your aptitude for analysis and expression; and
• your ability to present a reasoned case in proficient academic English.

Start or continue your application

You can start or return to an application using the relevant link below. As you complete the form, please  refer to the requirements above  and  consult our Application Guide for advice . You'll find the answers to most common queries in our FAQs.

Application Guide   Apply - Full time Apply - Part time

ADMISSION STATUS

Closed to applications for entry in 2024-25

Register to be notified via email when the next application cycle opens (for entry in 2025-26)

12:00 midday UK time on:

Friday 5 January 2024 Latest deadline for most Oxford scholarships Final application deadline for entry in 2024-25

*Three-year average (applications for entry in 2021-22 to 2023-24)

Further information and enquiries

This course is offered by the Oxford Internet Institute

• Course page on the institute's website
• Department open days
• Funding information from the institute
• Academic and research staff
• Research at the institute
• Social Sciences Division
• Residence requirements for full-time courses
• Postgraduate applicant privacy policy

Course-related enquiries

Advice about contacting the department can be found in the How to apply section of this page

✉ [email protected] ☎ +44 (0)1865 287210

Application-process enquiries

See the application guide

Other courses to consider

You may also wish to consider applying to other courses that are similar or related to this course:

View related courses

Visa eligibility for part-time study

We are unable to sponsor student visas for part-time study on this course. Part-time students may be able to attend on a visitor visa for short blocks of time only (and leave after each visit) and will need to remain based outside the UK.

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PhD by Distance

Our PhD by Distance programme allows you to benefit from our world-class support and the expertise of a Reading-based supervisor, while conducting your research in a location that suits your circumstances.

The programme is available to candidates who need to study for most of their registration period at another site, whether in the UK, or worldwide. Acceptance for PhD by Distance is subject to the approval of the supervisors and the Dean of Postgraduate Research Studies and Researcher Development.

There are several reasons why you may wish to base yourself away from the University of Reading while undertaking your PhD:

• the nature of your research project requires substantial access to resources and facilities located away from Reading
• you have employment commitments relevant to your research that prevent you from being based in Reading
• your PhD project has been agreed as part of a specific partnership/sponsorship arrangement.

You can choose to complete a PhD by Distance programme on either a part-time or full-time basis.

Please email [email protected]   for details.

What the programme offers

On the PhD by Distance programme, you will benefit from:

• supervision from one or more leading University of Reading academics, working at the forefront of their field
• access to a range of high-quality training, delivered on campus or online 
• access to extensive online Library resources
• a reduced tuition fee  set at half the standard full- or part-time rate for periods when studying off-campus for students starting in 2023/24. For students starting in 2024/25 onwards, the tuition fee will increase in line with on-campus fees
• a PhD qualification which is examined at the same level as a campus-based PhD and a standard PhD degree certificate which does not state the mode of study on it.

“During my PhD by Distance, I undertook my research in the field in South Africa. This experience enabled me to develop a much deeper and intricate understanding of my research topic, which would not have been possible if I had been based in Reading for most of the time.”

Third-year doctoral student

Eligibility

In addition to meeting the standard academic and language eligibility requirements, you must be able to demonstrate that you:

• can successfully conduct your research with the resources available to you at your off-campus study site
• have access to appropriate IT facilities, so that you can engage in supervision and training from your off-campus study site
• have the necessary time, commitment and appropriate attitude toward studying off-campus.

If you are intending to study in the Henley Business School, then please check with the relevant Department within the Business School about whether PhD by Distance is available before you apply.

• How to apply

Before starting your application, you are strongly advised to navigate to the PhD webpages of your chosen school or department  to read the specific guidance on how to apply, as the requirements can vary. Once you have read the guidance, you will need to make a formal application through the University's online application system , highlighting that you wish to study for a PhD by Distance (full or part-time). If you have questions about PhD by Distance in a specific school or department, then please contact the relevant School/Department PGR Administrator in the School PGR Support Team . 

Take the next step

• Get a prospectus
• Ask us a question

Distance Learning PhD

What is a distance learning phd.

A Distance Learning (DL) PhD allows you to undertake your postgraduate research degree at a location and time that fits with your current commitments. The DL PhD has the same outcome, and is conducted and examined with the same rigour and quality measures as those taking place on campus.

Whilst enrolled as a DL PhD Student you will not usually be required to visit the campus, and will instead meet with your supervisory team and other members of the University Research community via virtual means. You will receive a similar level of support as our on-campus PhD students, including supervision, training and development, seminars and access to support staff.

Distance Learning PhD requirements

Fitting part-time study into a busy life requires careful planning. You will need to work with your supervisory team from the outset to establish a weekly schedule, and you should plan to access the resources you need in good time. To support achievement, the University provides specialist resources, bespoke training and key events for distance learners.

Your DL PhD will take 6 years (part-time), and you will be required to meet certain milestones during each stage of your PhD, including the Annual and Major Reviews, submission of your Thesis, and the Viva Voce examination. These will all be completed using electronic means, and the Graduate School Development Programme offers training and guidance for these important milestones.

You will need to ensure that you have access to the required resources for your research project, and any specific arrangements for this must be agreed, for the duration of your study, in advance. You will also need access to an electronic device with internet access and video conferencing capabilities, in order to fully participate in online meetings and events.

Help and support

The Graduate School and your faculty will provide many opportunities to attend training and events; some of these will be vital for your research, and some will help you meet people and feel connected. You can also use the University Library's extensive electronic library and services for distance learners.

Although you will be based off campus, you can still access the University’s academic support and health and wellbeing support via email, phone, online chat or video call. Our IT support team can also help if you have questions about working online or accessing materials remotely.

The cost of a Distance Learning PhD in most of our subject areas is £2,250 a year (part-time) for UK/EU students, and £7,800 a year (part-time) for international students.

Check your research subject area page area page for more details.

How to apply

If you have an original idea for a research, you can find a PhD supervisor among our academics, whose expertise matches your own. You can also apply for one of our pre-approved PhD projects or explore our PhD subject areas and make an initial enquiry to our postgraduate research team.

When you apply for a research degree with us, you will need to submit a research proposal that outlines – among other things – how your project will be successfully undertaken at a distance, and demonstrate that you have access to the required resources, including IT equipment.

Apply from the relevant subject area page.

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Health Data Science

• Entry year 2024
• Duration Full time 36 Months

The PhD in Health Data Science provides research training in developing applied informatic and analytic approaches to data within health-related subjects such as medicine and the biomedical, biotechnological, and bioengineering sciences.

You will join the programme with a supervisory panel composed of academics working in health data science more broadly. Throughout the programme, and particularly during your first year, you will be encouraged to engage in training opportunities at Lancaster and elsewhere to develop both your research skills and subject-specific knowledge and abilities. Throughout your studies, you will focus on novel scientific research, developing best practice in interpreting and communicating new scientific methods and findings.

Your department

• Lancaster Medical School Faculty of Health and Medicine
• Telephone +44 (0)1524 592032

Entry requirements

Academic requirements.

2:1 Hons degree (UK or equivalent) in a relevant subject.

We may also consider non-standard applicants, please contact us for information.

If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.

Additional Requirements

As part of your application you will also need to provide a viable research proposal. Guidance for writing a research proposal can be found on our writing a research proposal webpage.

English Language Requirements

We may ask you to provide a recognised English language qualification, dependent upon your nationality and where you have studied previously.

We normally require an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 5.5 in each element of the test. We also consider other English language qualifications .

If your score is below our requirements, you may be eligible for one of our pre-sessional English language programmes .

Contact: Admissions Team +44 (0) 1524 592032 or email [email protected]

Fees and funding

The tuition fee for students with home fee status is set in line with the standard fee stipend provided by the UK Research Councils. The fee stipend for 2024/25 has not been set. For reference, the fee stipend for 2023/24 was full-time £4,712.

The international fee for new entrants in 2024/25 is full-time £26,490.

Depending on the nature of the research project, an additional programme cost may be charged. This additional fee will contribute towards the costs incurred on specific research projects. These costs could include purchasing specialist consumables, equipment access charges, fieldwork expenses and payments for transcription/translation services.  Normally any additional charge will not exceed a maximum of £9,720 but this could be increased in exceptional circumstances.

Applicants will be notified of any specific additional programme cost when the offer of a place is made.

General fees and funding information

There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.

Specific additional costs for studying at Lancaster are listed below.

College fees

Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small College Membership Fee  which supports the running of college events and activities. Students on some distance-learning courses are not liable to pay a college fee.

For students starting in 2023 and 2024, the fee is £40 for undergraduates and research students and £15 for students on one-year courses. Fees for students starting in 2025 have not yet been set.

Computer equipment and internet access

To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated  IT support helpdesk  is available in the event of any problems.

The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.

For most taught postgraduate applications there is a non-refundable application fee of £40. We cannot consider applications until this fee has been paid, as advised on our online secure payment system. There is no application fee for postgraduate research applications.

For some of our courses you will need to pay a deposit to accept your offer and secure your place. We will let you know in your offer letter if a deposit is required and you will be given a deadline date when this is due to be paid.

The fee that you pay will depend on whether you are considered to be a home or international student. Read more about how we assign your  fee status .

If you are studying on a programme of more than one year’s duration, the tuition fees for subsequent years of your programme are likely to increase each year. Read more about  fees in subsequent years .

Scholarships and bursaries

You may be eligible for the following funding opportunities, depending on your fee status and course. You will be automatically considered for our main scholarships and bursaries when you apply, so there's nothing extra that you need to do.

Unfortunately no scholarships and bursaries match your selection, but there are more listed on scholarships and bursaries page.

If you're considering postgraduate research you should look at our funded PhD opportunities .

We also have other, more specialised scholarships and bursaries - such as those for students from specific countries.

Browse Lancaster University's scholarships and bursaries .

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Health Studies

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Important Information

The information on this site relates primarily to 2024/2025 entry to the University and every effort has been taken to ensure the information is correct at the time of publication.

The University will use all reasonable effort to deliver the courses as described, but the University reserves the right to make changes to advertised courses. In exceptional circumstances that are beyond the University’s reasonable control (Force Majeure Events), we may need to amend the programmes and provision advertised. In this event, the University will take reasonable steps to minimise the disruption to your studies. If a course is withdrawn or if there are any fundamental changes to your course, we will give you reasonable notice and you will be entitled to request that you are considered for an alternative course or withdraw your application. You are advised to revisit our website for up-to-date course information before you submit your application.

More information on limits to the University’s liability can be found in our legal information .

Our Students’ Charter

We believe in the importance of a strong and productive partnership between our students and staff. In order to ensure your time at Lancaster is a positive experience we have worked with the Students’ Union to articulate this relationship and the standards to which the University and its students aspire. View our Charter and other policies .

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Professional Doctorate Data Science

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On-campus or Online

The first industrial doctorate of its kind will equip you with interdisciplinary research and practical skills for a job in data science or data analytics.

• Award ProfD
• Start date September 2024, January 2025
• Application deadline $value
• Duration Doctorate full-time: 36 months, Doctorate part-time: 72 months
• Mode of study full time, part time
• Delivery on campus

Our Professional Doctorate in Data Science is the first industrial doctorate of its kind, and is supported by The Data Lab innovation centre.

We build on Stirling’s highly successful taught MSc Data Science to equip you with a range of cutting-edge, interdisciplinary research and practical skills and tools, that will lead to an academic or industry job in the area of Data Science, with possible applications to sectors such as life-sciences, finance, engineering, computing, healthcare, fintech or business.

In addition to enhancing students’ employability through work-based learning, the doctorate prepares you to undertake interdisciplinary Data Science research, jointly supervised by world-leading Stirling academics and Data Science industry experts.

The Professional Doctorate consists of a one-year taught programme, based on Stirling MSc programmes in Data Science, and a two-year research programme, to be conducted in collaboration with an industrial partner around industry-relevant research questions. Students could be employees of the industrial partner looking for further training and qualification, or have already established a (potential) collaboration with an industrial partner willing to support the project.

Each of our MSc in Data Science or in Fintech may offer the opportunity to establish a suitable collaboration with an industrial partner, and then grant access to the second year of the Professional Doctorate in Data Science on a research programme agreed with the industrial partner.

Specific projects and collaborations can be considered on a case-by-case basis. An (in principle) agreement with an identified partner company is necessary for the research component of the program.

Top reasons to study with us

Course objectives.

This professional/industrial doctorate is designed to:

• Equip professionals with the required multi-disciplinary skills, and underlying theoretical, practical and transferable knowledge, to undertake practitioner-oriented, impact-led research in data science.
• Give sound training in relevant practical, investigative, analytical and generic skills required for research in the area of data science.
• Experience of data science challenges and applications in a wide range of areas, such as business, healthcare, life science, fintech and scientific disciplines.
• Provide the opportunity to plan, undertake and prepare publication quality research.

Work placements

The research component of the Professional Doctorate in Data Science is a project of industrial interest to be carried out in collaboration with a company supporting the project.

Flexible learning

If you’re interested in studying a module from this course, the Postgraduate Certificate or the Postgraduate Diploma then please email Graduate Admissions to discuss your course of study.

Faculty facilities

The Professional Doctorate can be attended both as a full time or part-time course. The taught component is organised around learning material provided online, contact teaching and tutorial hours, and an “open-door” approach allowing students a direct contact with lecturers, providing for great flexibility in the organisation of study. The research component consists of a research project whose development can be planned by agreement between the student, the company and the academic supervisor.

If you’re interested in studying a module from this course, the Postgraduate Certificate or the Postgraduate Diploma then please email Graduate Admissions to discuss your course of study.

Entry requirements

Academic requirements.

Students applying may have a variety of backgrounds including:

• numerate and computational degrees (computing, mathematics, physics, engineering)
• medical/clinical, business, marketing or economics background, plus some relevant work (industrial or commercial) experience

Students may also come from other science or engineering backgrounds, to gain applied research and analytical skills that are in high demand in the Scottish job market.

Students with suitable research-oriented Masters degrees in numerate and computational disciplines (computing, mathematics, physics, engineering), will be considered for direct entry to the second year of the Doctoral Training Component, on a case-by-case basis.

An established, in-principle or under-discussion agreement with an industrial partner interested in collaborating and supporting the research component of the programme should be in place.

International entry requirements

View the entry requirements for your country.

English language requirements

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:

• IELTS Academic or UKVI 6.0 with a minimum of 5.5 in each sub-skill.
• Pearson Test of English (Academic) 56 overall with a minimum of 51 in each sub-skill.
• IBT TOEFL 78 overall with a minimum of 17 in listening, 18 in reading, 20 in speaking and 17 in writing.

See our information on English language requirements for more details on the language tests we accept and options to waive these requirements.

Pre-sessional English language courses

If you need to improve your English language skills before you enter this course, our partner INTO University of Stirling offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for entry to this degree.

Find out more about our pre-sessional English language courses .

Course details

You will undertake a number of taught modules to equip you with the skills required for data science research. These modules are taught through lectures, practicals and small group work and are assessed through a variety of course work and exams.

Compulsory modules:

• Mathematical Foundations (10 credits)
• Statistics for Data Science (10 credits)
• Representing and Manipulating Data (20 credits)
• Commercial and Scientific applications (20 credits)
• Relational and non-relational databases (20 credits)
• Data Analytics (20 credits)
• Cluster Computing (20 credits)
• Research Dissertation project (60 credits)

To prepare for the professional doctorate, an independent research project (60 credits) will include a systematic review of an appropriately challenging applied research topic/area, and development of a full Doctorate research proposal as outputs – assessed through an oral viva exam and research poster presentation.

Following the taught component, you will undertake a period of industry-led applied research (360 level 12 credits) by working with experienced academic and industrial supervisors, on original piece(s) of an applied research project. The project could either be a single long project or a portfolio of data-centric projects, depending on the industrial organization’s strategic priority needs. Outcomes will be presented in a doctoral dissertation assessment through a viva examination by internal and external examiners.

The module information below provides an example of the types of course module you may study. The details listed are for the current academic year (September 2023). Modules and start dates are regularly reviewed and may be subject to change in future years.

Course Details

The taught component of the Professional Doctorate spans across the first year and mutates the modules from the various MSc in Data Science, and includes an advanced dissertation project with an assessment of the state of the art and research plan for the next two years.

The research component consists of a period of industry-led applied research, carried out by working with experienced academic and industrial supervisors, on original piece(s) of an applied research project. The project could either be a single long project or a portfolio of data-centric projects, depending on the industrial organisation’s strategic priority needs. Outcomes will be presented in a doctoral dissertation.  

Assessment of the taught component of the program follows the standard assessment of MSc modules and may consists of a variety of assessment strategies, including written assignments, exams,  individual projects, collaborative and group work, lab work, presentations and reports and a dissertation project.

The doctoral dissertation will be assessed through a viva examination by an internal and an external examiner (as in a PhD viva).

Assessment will be tailored to students’ special needs, where appropriate.

Course director

Dr Andrea Bracciali

[email protected] +44 (0)1786 467446

Fees and funding

Fees and costs.

This fee is charged as an annual course fee. If you need to extend your period of study or repeat study, you will be liable for additional fees. Your fees will be held at the same level throughout your course.

For more information on courses invoiced on an annual fee basis, please read our tuition fee policy .

Doctoral loans

If you're domiciled in England or Wales you may be eligible to apply for a doctoral loan from your regional body:

• English students can apply for a loan of up to £28,673 from  Student Finance England .
• Welsh students can apply for a loan of up to £28,395 from  Student Finance Wales .

Additional costs

There are some instances where additional fees may apply. Depending on your chosen course, you may need to pay additional costs, for example for field trips. Learn more about additional fees .

Scholarships and funding

Funding .

Eligible international students could receive a scholarship worth between £4,000-£7,000.  See our range of generous scholarships for international postgraduate students .

University of Stirling alumni will automatically be awarded a fee waiver for the first year of Masters studies through our Stirling Alumni Scholarship .

Applicants from the UK or Republic of Ireland who hold a first-class honours degree or equivalent will automatically be awarded a £2,000 scholarship through our  Postgraduate Merit Scholarship .

If you have the talent, ability and drive to study with us, we want to make sure you make the most of the opportunity – regardless of your financial circumstances.

Learn more about available funding opportunities or use our scholarship finder to explore our range of scholarships.

Cost of living

If you’re domiciled in the UK, you can typically apply to your relevant funding body for help with living costs. This usually takes the form of student loans, grants or bursaries, and the amount awarded depends upon your personal circumstances and household income.

International (including EU) students won’t normally be able to claim living support through SAAS or other UK public funding bodies. You should contact the relevant authority in your country to find out if you’re eligible to receive support.

Find out about the cost of living for students at Stirling

Payment options

We aim to be as flexible as possible, and offer a wide range of payment methods - including the option to pay fees by instalments. Learn more about how to pay

After you graduate

Demand for people with data science skills is projected to grow rapidly in the coming years attracting high salaries.

Our Professional Doctorate in Data Science is run in partnership with industry and is designed to produce graduates with the skills that companies need.

Employability skills

The Doctorate programme, equivalent to an Engineering Doctorate (EngD), is aimed at a clear and distinct market of professionals seeking to enhance their employability opportunities through applied, impact-led research. You’ll learn to develop and validate innovative, data-driven and evidence-based approaches within your chosen career. The programme is geared towards enhancing both your applied, multi-disciplinary research and employability skills in data science.

The doctorate is open to any profession where data-driven and data-intensive research, and its informational derivatives, are central to the development of sustainable business and industry models, including decision-making, project and risk evaluation, policy and technology development. The doctorate research component is relevant to the student’s professional setting and career aspirations.

Companies we work with

Stirling is a member of The Data Lab, which is an Innovation Centre with the aim of developing the data science talent and skills required by industry in Scotland. The Data Lab collaborates with the University of Stirling to help deliver the course, and provide funding and resources for students. You can find out more about the Data Lab from their web site .

We have also developed this professional doctorate in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Data Science projects to our students. Amazon’s development centre in Scotland is close by in Edinburgh. The first year of the course features a long Industry-led research dissertation project, generally in partnership with a company or technology provider. This provides students with a showcase of their skills to take to employers or launch online.

We also have a programme of invited speakers from industry who give the students a chance to ask questions of people who are doing data science every day. Recent companies have included MongoDB, SkyScanner and HSBC.

Related courses

• MSc Artificial Intelligence
• MSc Big Data
• MSc Big Data (Online)
• MSc Business Analytics
• MSc Data Science for Business
• MSc Finance and Data Analytics
• MSc Financial Technology (FinTech)
• MSc Marketing Analytics
• MSc Mathematics and Data Science
• MSc Social Statistics and Social Research

Which course would you like to apply for?

Search for another course

Statistical Science MPhil/PhD

London, Bloomsbury

An MPhil/PhD in Statistical Science obtained at UCL will equip you with the necessary research skills to thrive in the modern era of Big Data and Artificial Intelligence. Familiarity with state-of-the-art research methodology in a range of areas, including Statistical Modelling, Data Analysis and Computational Algorithms, places graduates of our programme at the forefront of a highly contemporary and dynamic field.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

• Entry requirements

A minimum of an upper second-class UK Bachelor's degree, or a UK Master's degree in statistics, mathematics, computer science or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable.

The English language level for this programme is: Level 1

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

The demand for numerate graduates exceeds the supply in most areas. Many new and existing opportunities in industry, medicine, government, commerce, or research await science graduates who have supplemented their first degree with additional training in quantitative skills, such as those provided by the postgraduate programmes available within the Department of Statistical Science.

Who this course is for

This programme is best suited to those aiming for a research degree and/or an academic career in Statistics, Data Science and other related fields.

What this course will give you

While the department offers world-class expertise along with strong links to practitioners, its position within UCL provides a large breadth of research specialisations. Besides ties to other mathematical sciences, the department collaborates with researchers in a number of fields, including computer science, environmental science, engineering, management, finance, biology and medicine.

The opportunity to engage with leading researchers across disciplines while accessing London-based government and industry figures gives UCL students a distinct advantage.

More intangibly, by being in a truly multidisciplinary environment, UCL students gain an appreciation for knowledge and its societal impact. This leads not only to new insights but also to a readiness to critique the established order, which is both intellectually and personally fulfilling.

The foundation of your career

Destinations after graduation include Universities, the Healthcare Sector, Finance organisations, Consulting organisations, Commerce organisations.

Employability

Graduates of the PhD programme are well placed to continue as researchers in both academia and the private sector. In particular, greater data collection has created a demand for enhanced methodologies for analysis, which is a strength of most recent graduates.

The department has strong connections with several research organisations, for example the UCL Centre for Artificial Intelligence, the UCL Medical School and the Biomedical Research Centre, the Gatsby Computational Neuroscience Unit and the UCL Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX). The department contributes to the UCL Centre for Doctoral Training in Data Intensive Science, the UCL Centre for Doctoral Training in Foundational Artificial Intelligence and to the cross-institutional Health Data Research UK-Turing Wellcome PhD Programme in Health Data Science. The Department is a partner in the London NERC Doctoral Training Partnership. UCL was a founding member of the Alan Turing Institute for Data Science, and continues to play a major role in the Institute’s activities.  

Staff members also collaborate directly with hospitals, power companies, government regulators, the financial sector and several other organisations.

Consequently, research students have ample opportunity to engage with external institutions in order to frame their work.

Teaching and learning

There are no specific requirements in terms of courses to be attended during a PhD degree.

Students are initially registered for the MPhil degree. No sooner than nine months after registration, they are transferred to the PhD degree with retrospective effect if they show a capacity for original work. This will require the preparation of a substantial upgrade report describing the existing work in the area of investigation, giving details of the original work that they have performed so far, and setting out a plan for the remaining period of their research. It will also involve a viva.

The research degree programme is a self-directed programme under the supervision of academic experts. You should manage your time for research activities by discussing with your supervisor(s). You can arrange a regular meeting with your supervisor(s). The supervisor meetings usually take place once per week, depending on the status of your research.

Research areas and structure

The department’s methodological research is organised into six themes:

• Biostatistics
• Computational statistics
• Economics, finance and business
• General theory and methodology
• Multivariate and high dimensional data
• Stochastic modelling and time series

Research often cuts across these themes. For example, externally funded projects in the following application areas are in progress:

• Cognitive neuroscience
• Econometrics and finance
• Epidemology
• Environmetrics and hydrology
• Machine learning
• Population and systems biology
• Statistical imaging

Much of this work is interdisciplinary and involves collaborations within and outside UCL.

Research environment

The Department of Statistical Science has played a major role in the development of the subject since its foundation in 1911 as the first department of statistics in the world, with Karl Pearson as its head. Since then, many famous names in statistics have been associated with the department, including Egon Pearson, R. A. Fisher and Jerzy Neyman. Today the Department is among the three largest statistics groups in the UK with more than 40 academic members of staff. .

We carry out research across a wide range of theoretical and applied areas. The main areas of interest are organised into six themes: Biostatistics; Computational statistics; Economics, finance and business; Environmental statistics; General theory and methodology; and Multivariate and high dimensional data. In addition, there are organised research groups in the areas of Probability, Methodology for Weather and Climate and Statistics for Health Economic Evaluation. In the last Research Excellence Framework exercise (2021/22), over 97% of our research output was classified as “worldleading” or “internationally excellent” in terms of originality, significance and rigour.

The department has strong connections with several research organisations, such as the UCL Centre for Artificial Intelligence, the UCL Medical School and the Biomedical Research Centre, the Gatsby Computational Neuroscience Unit and the UCL Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX). The department contributes to the UCL Centre for Doctoral Training in Data Intensive Science, the UCL Centre for Doctoral Training in Foundational Artificial Intelligence and to the cross-institutional Health Data Research UK-Turing Wellcome PhD Programme in Health Data Science. The Department is a partner in the London NERC Doctoral Training Partnership.

UCL was a founding member of the Alan Turing Institute for Data Science, and continues to play a major role in the Institute’s activities.

Staff members also collaborate directly with hospitals, power companies, government regulators, the financial sector and several other organisations. 

You are initially registered for the MPhil degree. No sooner than nine months after registration, you are transferred to the PhD degree with retrospective effect if you show a capacity for original work. This will require the preparation of a substantial upgrade report describing the existing work in the area of investigation, giving details of the original work that you have performed so far, and setting out a plan for the remaining period of your research. It will also involve a viva.

The typical length of the PhD programme is three years for full-time students and five years for part-time students; an MPhil is expected to be achieved in a shorter period. If you are not ready to submit at the end of the third year, you may be able to register as a completing research student (CRS) while you write up your thesis.

The MPhil/PhD has no required curriculum. However, you are expected to agree on a customised programme of study with your supervisor, which may involve specialisation courses (either at UCL or externally, for example, at the London Taught Course Centre or Academy for PhD Training in Statistics) or independent reading. Attendance at research seminars is encouraged, and after you have been upgraded to PhD status you are required to present your research in a seminar stream dedicated to this purpose. Finally, the UCL Doctoral School has its own requirements for training courses. For instance, you are required to attend Research Integrity Training.

The typical length of the PhD programme is three years for full-time students and five years for part-time students; an MPhil is expected to be achieved in a shorter period. If you are not ready to submit at the end of the third year, you may be able to register as a completing research student (CRS) while you write up your thesis.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

T here are no programme-specific costs.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

Research Council funding may be available for UK and Overseas nationals. Other funding opportunities may also be available. For details visit www.ucl.ac.uk/statistics/prospective-postgraduates/studentships

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

CSC-UCL Joint Research Scholarship

Value: Fees, maintenance and travel (Duration of programme) Criteria Based on academic merit Eligibility: EU, Overseas

Deadlines and start dates are usually dictated by funding arrangements so check with the department or academic unit to see if you need to consider these in your application preparation. In most cases you should identify and contact potential supervisors before making your application. For more information see our How to apply page.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2024-2025

Year of entry: 2023-2024, got questions get in touch.

Statistical Science

[email protected]

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PhD in Data Science

Information about the PhD in Data Science.

Progression to the PhD in Data Science

Progression from the first year MSc by Research to your PhD is contingent on making satisfactory academic progress during the first year. In general, if you have a mark of 65% or above on both your coursework and dissertation, we will consider this evidence of satisfactory progress. If your marks are below this, you may still be allowed to progress, but only if we can be otherwise satisfied that you have the ability to successfully complete a PhD. These decisions will be taken by the CDT in Data Science Executive Committee, most likely towards the end of August.

Data Science Executive Committee

In addition to the successful completion of the MSc by Research programme (Year 1), progression to the PhD in Data Science is contingent on the approval of an outline PhD project proposal proposal within the first few months of Year 2. Guidelines and deadline dates can be found here:

Preparing & submitting your outline PhD project proposal

Actions to take once your outline PhD project proposal has been approved

PhD Guidelines & Milestones

After the approval of their PhD projects, CDT in Data Science students are governed by the same procedures as any other PhD student, as described in the Informatics Graduate School (IGS) webpages, and should ensure they meet the guidelines as outlined in the Monitoring links below:

• Information for Informatics PhD students
• PhD yearly timelines
• Formal student monitoring
• Researcher's handbook
• Archived PhD theses
• Submitting your thesis
• Finally, when you leave...

It is your responsibility to ensure you meet the requirements and milestones for PhD students and should discuss any questions you have with your supervisor(s).

Pastoral Care: Your supervisors, and/or your 3rd panel member, can also provide you with pastoral support should you need it at any point during your research.  In addition the deputy director of the CDT has the primary role of representing student interests and providing a one-to-one point of contact for any CDT student who has any issue they wish to talk about. You also have access to any member of staff in the Graduate School, in particular the Head of Student Services, the Deputy Head of Graduate School and CDT Administrator.

Student Counselling: https://www.ed.ac.uk/student-counselling

Resolving Problems: http://web.inf.ed.ac.uk/infweb/student-services/igs/phd/student-support/resolving-problems

Data Science

Designed to develop core skills in data science, the programme covers a mix of practical and theoretical issues integral to careers in many data driven sectors. Students will learn how to approach real-world data problems, applying their newfound skills in critical thinking, problem solving and analysis.

Key information

• PgCert, PgDip, and MSc options available for self-paced study.
• Start in January or September 2024.
• Learn sought-after coding skills, including Python.

Register your interest Find out more and apply

What you will study

• The programme will teach research methods in data science and help you to understand contemporary issues in the field.
• You will discover methods of datamining, from theory to practical understanding.
• The programme will help you to understand how to create effective information visualisations and how to engage critically with visual displays of data.
• You will employ the full Data Science workflow from data acquisition and processing, through model development and selection, to final deployment and maintenance.
• You will learn optimisation techniques, how to curate and utilise large quantities of data, and how to model and simulate complex systems of data.

How will you be taught?

• You will learn through project and practical work, helping you to understand real-world applications of Data Science.
• Learn through a mix of led and independent study, with synchronous and asynchronous teaching.
• You will have the opportunity to engage in a weekly live panel discussion that will cover data science concepts and specific questions from students.
• Students on the MSc course will undertake a dissertation in data science. Projects based at an employer or sponsor are welcomed (subject to eligibility criteria).

Learning outcomes

Leadership skills and project management.

Cultivate your leadership skills, building your ability to reflect on feedback, to manage projects, and to persevere in the face of challenges.

Problem solving, critical thinking, and analytical skills

Improve your data literacy, whilst learning to think creatively and use your imagination to formulate, design and develop innovative approaches to data analysis.

Knowledge and understanding of programming

Develop specific coding skills in areas such as Excel, Python, Mathematica, Matlab, and mapping.

The Data Science programme will help learners to prepare for, and develop in, a range of careers in data driven sectors and industries. Graduates will leave with sought-after skills across practical and theoretical aspects of data science.

How we can help you to advance your career

Join our virtual information session.

Meet our staff, learn more, and ask questions about how our courses can work for you.

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• Cambridge-Turing sessions reloaded: collaborative data science and AI research
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Cambridge centre for data-driven discovery, currently advertised phd studentships.

• The majority of current PhD studentships are listed on the  University's Jobs site
• For a full list of departments and faculties at the University, visit this page where you can learn more about the research interests within each department
• To find academics you might like to work with, use our directory

Graduate Admissions

The  Graduate Admissions  office provides a range of information on postgraduate programmes at Cambridge, along with a step-by-step guide to the application process. It is advisable to start researching funding opportunities at least a year before your course begins.

MPhil and PhD course relevant to data science - from across University of Cambridge

Please visit the relevant pages and contact the relevant education provider if you have queries. You should pay particular attention to the entry requirements and guidance for applicants there.

MPhil in Machine Learning and Machine Intelligence - an eleven month full-time programme offered by the Machine Learning Group, the Speech Group, and the Computer Vision and Robotics Group in the Cambridge University Department of Engineering.  The course aims to teach the state-of-the-art in machine learning, speech and language processing, and computer vision; to give students the skills and expertise necessary to take leading roles in industry and to equip them with the research skills necessary for doctoral study at Cambridge and other universities.

PhD programme in Advanced Machine Learning - The Machine Learning Group is based in the Department of Engineering, and encourages applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. 

Cambridge Centre for AI in Medicine - Cambridge Centre for AI in Medicine (CCAIM) is a multi-disciplinary centre established by the University of Cambridge in 2020 to develop pioneering AI machine learning (ML) technologies that will transform biomedical science, medicine and healthcare. PhD studentships are oten available, please check their website for details.

SynTech Centre for Doctoral Training - EPSRC Centre for Doctoral Training in Next Generation Synthetic Chemistry Enabled by Digital Molecular Technologies. An interdisciplinary cohort-driven programme to produce the next generation of molecule making scientists by combining Synthetic Chemistry, Chemical Engineering, Engineering, Machine Learning and Artificial Intelligence.

Advanced Computer Science MPhil  - The MPhil in Advanced Computer Science (the ACS) is designed to prepare students for doctoral research, whether at Cambridge or elsewhere. Typical applicants will have undertaken a first degree in computer science or an equivalent subject, and will be expected to be familiar with basic concepts and practices. The ACS is a nine–month course which starts in early October and finishes on 30 June. It covers advanced material in both theoretical and practical areas as well as instilling the elements of research practice.

Application of Artificial Intelligence to the study of Environmental Risks MRes and PhD - The UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) trains researchers (through several multidisciplinary cohorts) to be uniquely equipped to develop and apply leading-edge computational approaches to address critical global environmental challenges by exploiting vast, diverse and often currently untapped environmental data sets. Embedded in the outstanding research environments of the University of Cambridge and the British Antarctic Survey (BAS), the AI4ER CDT addresses problems that are relevant to  building resilience to environmental hazards and managing environmental change .

Postgraduate Study in Mathematics - Various postgraduate courses of a mathematical nature are available at the University of Cambridge, including both taught courses and research degrees.

Mathematics of Information PhD  - This cutting-edge training Centre in the Mathematics of Information produces a new generation of leaders in the theory and practice of modern data science, with an emphasis on the mathematical underpinnings of this new scientific field. The Cambridge Mathematics of Information (CMI) PhD is a four-year course leading to a single PhD thesis.

Cambridge Computational Biology Institute MPhil and PhD ​ - The MPhil in Computational Biology course is aimed at introducing students in the biological, mathematical and physical sciences to quantitative aspects of modern biology and medicine, including bioinformatics. The course has been developed by the Cambridge Computational Biology Institute and is run by the Department of Applied Mathematics and Theoretical Physics at the Centre for Mathematical Sciences (CMS).

Centre for Scientific Computing MPhil and PhD  - The MPhil programme on Scientific Computing is offered by the University of Cambridge as a full-time course which aims to provide education of the highest quality at Master’s level. A common route for admission into our PhD programme is via the Centre’s MPhil programme in Scientific Computing.

Part III Mathematics  - Part III is a 9 month taught masters course in mathematics.  It is an excellent preparation for mathematical research and it is also a valuable course in mathematics and in its applications for those who want further training before taking posts in industry, teaching, or research establishments. Students admitted from outside Cambridge to Part III study towards the Master of Advanced Study (MASt).  Students continuing from the Cambridge Tripos for a fourth year, study towards the Master of Mathematics (MMath).  The requirements and course structure for Part III are the same for all students irrespective of whether they are studying for the MASt or MMath degree. There are over 200 Part III (MASt and MMath) students each year; almost all are in their fourth or fifth year of university studies. 

School of Clinical Medicine Graduate Training Office - Prospective students interested in pursuing a graduate degree course in a subject area related to clinical medicine at the University of Cambridge should consult the School’s individual departmental websites for detailed information about the courses which they run and the University’s Graduate Admissions website for information on the application process and on funding opportunities.

Centre for Doctoral Training in Data, Risk And Environmental Analytical Methods  - The CDT embraces a wide range of world-leading Doctoral research in the area of Big Data and Environmental Risk Mitigation. The CDT research underway seeks to utilise emerging technologies, techniques and tools, to more accurately monitor the environment, enabling cutting edge research. To provide end-users with more integrated information at improved temporal and spatial resolutions to deliver solutions to environmental challenges (both acute and long- term). Funded by  NERC  (the Natural Environment Research Council, NERC Ref: NE/M009009/1), the DREAM (Data, Risk and Environmental Analytical Methods) consortium is made up of Cranfield, Newcastle, Cambridge and Birmingham universities.

Centre for Doctoral Training in Data Intensive Science  - The Cambridge CDT in Data Intensive Science is an innovative, interdisciplinary centre, distributed between the Department of Physics (Cavendish Laboratory), Department of Applied Mathematics and Theoretical Physics (DAMTP), Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Institute of Astronomy (IoA).

MPhil in Data Intensive Science - This course aims to take science graduates and to prepare them for data intensive research careers by providing advanced training in three key areas – Statistical Analysis, Machine Learning, and Research Computing – and their application to current research frontiers.

Cambridge Digital Humanities - The MPhil provides the opportunity to specialise in a chosen subject area as well as an advanced level introduction to DH approaches, methods and theory. The course provides critical and practical literacy, the chance to advance an extant specialization by re-contextualizing it in relation to advanced theoretical work, and the chance to develop as a DH scholar.

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

• Supports and connects the growing data science and AI research community 
• Builds research capacity in data science and AI to tackle complex issues 
• Drives new research challenges through collaborative research projects 
• Promotes and provides opportunities for knowledge transfer 
• Identifies and provides training courses for students, academics, industry and the third sector 
• Serves as a gateway for external organisations 

• Visit the Gateway
• Visit the Alliance
• Visit HDR UK Futures

HDR UK-Turing Wellcome PhD Programme in Health Data Science

This truly outstanding and generously funded four-year programme at top UK universities provides you a pathway to join the UK’s leaders in health data research.

What this unique PhD programme offers you

Four-year programme: An initial foundation year allows students to gain real experience and insight into health data research.

Hosted by leading universities: Our host universities are among the very best in health data research.

Nurturing each student: Our programme aims to identify the particular abilities and interests of each student, and gear their PhD experience to effectively develop them.

Leadership Programme: Students benefit from a bespoke expert-led programme to develop the skills they need to understand, collaborate and influence others.

Generous funding: Students have their tuition fees (UK Home rate), college fees (where applicable), research expenses and travel costs paid and receive an enhanced, tax-free stipend with increases every year. (Y1 outside London: £23,955, Y1 in London: £25,954)

Building networks and experience: We actively support students in building networks and contacts in academia, the NHS and industry as well as taking internships and other opportunities to gain real-world experience. This includes a post-PhD bursary to support your next career step.

Team spirit: Strong relationships are built between our entire cohort of students through joint activities that build a genuine team spirit.

Personal support:  Each student has their own Director of Studies who is an additional point of contact during their time with us. All students are also further supported by the PhD team.

“The PhD programme has enabled me to gain first-hand experience in modern health data science approaches. It’s a truly unrivalled opportunity.”  Steven Wambua

Who is the PhD programme for?

We recruit enthusiastic, talented students who want to use data-driven research to develop and shape the UK’s response to the most complex health challenges of our times.

Applicants must have (or be on track to obtain):

• A first class or 2:1 undergraduate degree in statistics, mathematics, computer / data science, physics or an allied subject  or
• Another undergraduate degree subject and outcome but can demonstrate their suitability for this programme through additional qualifications or research experience.

Active or currently registered health care professionals   are not eligible and should consider the Wellcome PhD Fellowships for Health Professionals .

Applicants also need to meet the following criteria:

• Successful admission to the specified degree programme at one of our partner universities. Students will be expected to meet the admissions requirements of that department and university but do not need to hold the offer at the point of application.
• Two satisfactory academic or relevant references.
• Proof of a legal right to study in the UK or ability to satisfy the current requirements of UK Visa and Immigration.

Training is in-person, hybrid and virtual throughout the first year.

We are committed to a diverse and inclusive research culture . We welcome those who are returning from the workplace, international candidates and everyone underrepresented in STEM and academia. For further details see our FAQs .

We cannot accept applicants who are looking for a part-time PhD or those who are aiming to study whilst continuing to be employed elsewhere.

We aim to accommodate specific needs and personal circumstances. Please make us aware of individual circumstances when applying or contact us directly at  [email protected] . Please note our  applicant privacy notice .

If you have questions or require adjustments to the application process, please contact us below via email or telephone (+44 (0)770 847 8846).

There are no nationality restrictions and international students are able to apply. However, applicants are advised the award only covers fees at the UK/Home level. International students will be required to secure an additional scholarship from our partner universities (after receiving a offer from us at interview) to cover the difference between Home and Overseas fees. This will limit the university choices available:

(Please be aware that these are usually highly competitive and will need to be applied for separately in your application to the university post-offer. A successful application to the PhD programme does not guarantee a fee waiver or scholarship. We do not accept applications from candidates who are self-funding.)

We are currently only recruiting for Queen’s University Belfast.

These are only initial programmes of study for Year 1. Students may transfer to a new university programme from Year 2 after research projects have been confirmed.

Is this the PhD future for you?

Watch our Applicant Open Day hosted by our current students to find out more about the programme and whether it’s for you.

Applications are currently: Open

There is  one studentship available for October 2024 entry, based at Queen’s University Belfast. Applications are currently accepted through our application portal below:

Begin your application here

Deadline: Sunday 14 April 2024 at 23:59

The application process

Details required:

• Contact details
• Details and transcripts of university qualification(s)
• Any relevant job history
• Answers to personal-statement type questions
• Contact details for two referees
• There is no need to apply to universities, submit a research proposal, provide IELTS scores or contact supervisors at this stage

Submitted applications will first be checked for eligibility and then will undergo a first stage review. This will involve triage by the PhD Team in April 2024 . Successful applicants will be invited to an interview in May 2024 .

After receiving an offer, applications will be invited to apply to Queen’s University Belfast.

Selection criteria

Applicants should demonstrate that they meet the following criteria:

* These criteria will be assessed at interview via a pre-interview exercise.

HDR UK reserves the right to reject applicants who do not meet the criteria at any stage. Regretfully, we can only provide feedback for candidates who reach interview.

Programme Structure

The four-year programme is divided in two. There is an initial Foundation Year followed by a three-year research project. The first year combines the best in university-based training with HDR UK-led national activities. And we support students to produce game-changing research plans and their projects are backed by substantial research funding.

Foundation year

3-5 day immersion events allow students to gain insight into the work of HDR UK, and our academic, clinical and industry partners. Courses may be residential (expenses provided) with up to a week away from their home university or online. Students undertake an intensive deep dive into an important area of health data science. Immersion topics include risk prediction, oncology, clinical trials, epidemiology and bioinformatics. Past immersion weeks have been hosted by the Universities of Birmingham, Manchester, Oxford and University College London and the European Bioinformatics Institute.

The immersion events encourage students to work together and stimulate new interactions:

• Axes of Prognosis
• The Different Facets of Data

Research areas

PhD research projects can be linked to The Institute’s:

• Research priorities
• Research hubs
• Partnerships

Team working

Students operate as a national cohort and work collaboratively with others, overcoming traditional institutional silos. Students are registered with a  partner university  but can draw on academic expertise from across the HDR UK network and are supported to formulate research activities that bring together experts from across the UK.

• You can contact us at [email protected]   or phone (+44 (0)770 847 8846). 
• For details of how we process applicants’ data see PhD Applicant Privacy Notice .

Students have access to graduate-level courses and research project rotation in their university to introduce them to different areas of health data science and enable them to develop a bespoke research project under the guidance of our expert university leads.

Regular workshops and short courses introduce students to the work of HDR UK experts across our hubs, themes and priority areas and to external organisations. Past contributors have included NHSX, IQVIA and AstraZeneca.

Immersion and workshop events allow students to better understand the wider health and social care landscape and accelerate their potential to become sector leaders. They also enable students to develop more ambitious PhD research projects by stimulating collaboration with external academics, industry-based organisations, or by using national data infrastructure.

Training is provided by academic, industry and NHS experts to promote personal and professional development in leadership capability, cross-sector collaborative skills and inter-disciplinary working. In particular, HDR UK is committed to working with public and patients to build increased trust in health data research as well as designing solutions focused on improving patient outcomes and experience. Students will develop communication and collaborative skills to help put them at the forefront of this mission.

At the end of the Foundation Year students design a bespoke three-year research project and a multi-disciplinary supervision team based on their training experiences.

Research proposals will be rigorously reviewed by expert academics and public-patient representatives to ensure they are of the highest standards in terms of ambition, scientific methodology and impact on patient outcomes.

The research will be carried out at their home university and could be linked to HDR UK  research priorities ,  research hubs  or  partnerships .

This includes short immersions plus  longer practical real-world projects with businesses and other organisations at the cutting edge of everything from medical devices, to life sciences, to vaccines. Students also learn about leadership theory and attend specially-convened seminars from senior figures in relevant areas of healthcare.

Networks and experience: Students will be actively supported in building networks and contacts in academia, the NHS and industry as well as taking internships and other opportunities to gain real-world experience.

Team working: Students operate as a national cohort, building strong relationships through joint activities and overcoming traditional institutional silos.

Workshops: Regular workshops and short courses introduce the work of HDR UK experts and to external organisations.

Immersion events: These allow students to better understand the wider health and social care landscape and accelerate students’ potential to become a sector leader. They also enable them to develop an ambitious PhD research project.

Researcher development: Training is provided by academic, industry and NHS experts to promote personal and professional development in cross-sector collaborative skills, communication and inter-disciplinary working.

“Our Leadership Programme will give PhD students the chance to develop the practical skills they need to bring people together to use health data science to deliver much-needed innovations and advances in health and care,”  Professor Peter Bannister

Our partners

Programme partners include NHS Digital, AstraZeneca, Moorfields Eye Hospital NHS Foundation Trust, and University Hospitals Birmingham.

More broadly it will work with winners of the NHSX AI Innovation Award , which funds and supports promising artificial intelligence technologies in health and care. There will also be opportunities with businesses on the DTI listed top 100 digital health innovators which are using big data for healthcare innovation.

Master’s Degree Scholarships

We offer 10 annual Master’s degree scholarships worth £10,000 for students with an interest in dementia or diabetes research.

Undergraduate Summer Internship in Health Data Research

Apply for a summer work placement in health data research at a UK research organisation, with an HDR UK-Wellcome Biomedical Vacation Scholarship

Join the HDR UK Alumni Network

HDR UK’s online Alumni Network brings together the amazing people who have been part of our training and education programmes.

Our host universities

- - - - Meet our PhD students

Our PhD students come from a wide range of backgrounds - discover who they are and what their experiences have been as part of the PhD programme

Meet the PhD Programme team

Our wider team consists of leading experts in disciplines including theoretical physics, computer science, mathematics and statistics, applied mathematics and biochemistry.

• Miguel Bernabeu – University of Edinburgh
• Ioanna Manolopoulou – University College London
• Niels Peek – University of Manchester
• Iain Styles – Queen’s University Belfast
• Paul Taylor – University College London
• Catalina Vallejos – University of Edinburgh
• Angela Wood – University of Cambridge
• David Wong – University of Manchester
• Tom Nichols – University of Oxford
• Magnus Rattray – University of Manchester

Study with us in May

We're here to support you, every step of the way.

Advertise a vacancy on our platform today.

Read about our Research Excellence Framework submissions and results

In 2024 UEL celebrates a Year of Science

• All results

Data Science Prof Doc

This course is in clearing with spaces available

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The Professional Doctorate in Data Science (D.DataSc) is aimed at professionals who wish to enhance and/or validate data-centric, evidence-based approaches within their chosen career through a combination of taught modules and doctoral research.

The programme is delivered:

• Full-time, three years: one year of taught modules and two years of research
• Part-time, five years:  two years of taught modules and three years of research

A cross-disciplinary approach is central to the delivery of this programme and is therefore suitable for professionals in a broad range of professional disciplines and areas of employment.

"The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it - that's going to be a hugely important skill in the next decades." (Hal Varian, Chief Economist at Google).

The programme is unique, international, and ground-breaking in offering a Professional Doctorate qualification in Data Science. D.DataSc is an earned doctorate that allows the holder to use the title 'Dr'.

This course is only eligible for part-time student visa sponsorship. For more details about the restrictions of part time student visas please see our Student Visa page .

Find out more

• Undergraduate open days
• Undergraduate prospectus
• Postgraduate open days
• Postgraduate prospectus
• Make an enquiry Close

Course options

• September 2024

Professional Doctorate

Entry requirements, academic requirements, accepted qualifications.

Bachelor's degree with Upper Second Class (2:1) in Physical Science, Electrical, Electronic, Communication Engineering or Humanities and Social Science related subject.

International Qualifications

We accept a wide range of European and international qualifications in addition to A-levels, the International Baccalaureate and BTEC qualifications. Please visit our International page for full details.

English Language requirements

Overall IELTS 6.5 with a minimum of 6.0 in Writing, Speaking, Reading and Listening (or recognised equivalent). If you do not meet the academic English language requirements for your course, you may be eligible to enrol onto a pre-sessional English course .

The length of the course will depend on your current level of English and the requirements for your degree programme. We offer a 5-week and an 10-week pre-sessional course.

Mature applicants and those without formal qualifications

As an inclusive university, we recognise those who have been out of education for some time may not have the formal qualifications usually required. We welcome applications from those who can demonstrate their enthusiasm and commitment to study and have the relevant life/work experience that equips them to succeed on the course. We will assess this from the information provided in your application or may request additional information such as a CV or attendance at an interview. Please note that some courses require applicants to meet the entry requirements outlined.

Admissions policy / Terms of Admittance

We are committed to fair admissions and access by recruiting students regardless of their social, cultural or economic background. Our admissions policy sets out the principles and procedures we use to admit new students for all courses offered by the university and its partners.

Further advice and guidance

You can speak to a member of our Applicant Enquiries team on +44 (0)20 8223 3333, Monday to Friday from 9am to 5pm. Alternatively, you can visit our Information, Advice and Guidance centre.

Prof Doc Data Science

• Home Applicant
• Full time , 3 years
• 10200 First year fees £10,200 (taught element), then £6,020 per year for the next two research years.
• Part time , 5 years
• 1700 First year fees £1,700 (taught element) per 30 credit module, then £3,010 per year for the next three research years.
• International Applicant
• 15960 First year fees £15,960 (taught element), then £16,100 per year for the next two research years.
• 2660 First year fees £2,660 (taught element) per 30 credit module, then £8,050 per year for the next three research years.

Fees, funding and additional costs

EU, EEA and Swiss Nationals starting a course from September 2021, will no longer be eligible for Home fees. However, such nationals benefitting from Settled Status or Citizens' Rights may become eligible for Home fees as and when the UK Government confirms any new fee regulations.  Further information can be found at UKCISA .

Tuition fees are subject to annual change. Fees for future years will be published in due course.

Home students

Postgraduate loans scheme.

£10,280 to fund your Masters Programme under the Postgraduate Loans (PGL) scheme

Postgraduate Loans (PGL)

The Postgraduate Loan (PGL) provide non means-tested loans of up to £10,906 to taught and research masters students.  It will be paid to students as a contribution towards tuition fees, living costs and other course costs. Applications are made directly through  Student Finance England  

Eligibility

Whether you qualify depends on: •    if you've studied on a postgraduate course before •    your course •    your age •    your nationality or residency status

Full eligibility can be found   on the Government's Postgraduate Loan webpage .

Please take a look at the  Postgraduate Loans  for an overview of the new funding.

Postgraduate Scholarship

Apply for a 50 per cent discount on your tuition fees! You can get a 50 per cent discount on course fees through a UEL Postgraduate Scholarship. The scholarship is open to full-time and part-time UK and EU students of taught postgraduate courses. *Exclusions apply.

Find out more about full eligibility criteria and how to apply .

Terms and conditions apply.

Our scholarships and bursaries can help you

How we can help you

Did you know that with a postgraduate qualification you can expect to earn more than someone who only holds an undergraduate degree?

If you want to build new skills, change career paths, or further your career prospects, a postgraduate degree can help you. Our range of scholarships and bursaries will make financing your education that much easier. Below is some of the funding available to support you in your studies:

• Alumni Discount   - up to 15% fee waiver *exclusions apply. Please see  Alumni Discount page  for information.
• Early Payment Discount  - 5% fee waiver
• Asylum Seekers scholarship   - 100% fee waiver
• Civic Engagement - £1,000
• Hardship Bursary - up to £2,000
• Sport Scholarships   - Up to £6,000

How to pay your fees

There are a number of ways you can pay your fees to UEL

• Online payment facilities
• By telephone
• In person at our Docklands or Stratford campus
• Bank transfer

Full information on making payments can be found  on our Finance page

If you wish to discuss payments to the University, please contact our Income Team on 020 8223 2974 or you can email  [email protected]

Ideas for funding your postgraduate study

Below are some ideas on how to fund your postgraduate study:

•     Apply for a   Postgraduate Loan  
•     Take advantage of   UEL scholarships and bursaries
•     Ask your employer to sponsor your study
•     Study part-time so you can work at the same time (applicable to courses that have a part-time mode)
•     Look at  UK Research and Innovation funding options

The Student Money Advice and Rights Team (SMART) are here to help you navigate your finances while you're a student at the University of East London. We can give you advice, information and guidance on government and university funds so that you receive your full funding entitlement. Live chat: Click the live chat icon in the bottom left of the screen Phone: 020 8223 4444

International students

Living costs for international students.

As part of the Tier 4 student visa requirements, UK Visas and Immigration (UKVI) estimate that you will need £1,265* per month to cover your living costs. It includes expenses for accommodation, food and drink, travel within London, text books, entertainment, clothing, toiletries and laundry. Most Tier 4 students are required to show they have sufficient funds to cover the first nine months of the course before they start - a total of £11,385 - in addition to the tuition fees. You can find more information about the specific requirements of the Tier 4 student visa . The amount that you will spend can vary depending on your lifestyle. The UKCISA International Student Calculator can help you plan and manage your money.

* Please note the Immigration Rules are subject to change and this figure is likely to be increased by UKVI year on year. Please therefore check our ISA page for more information at the time of preparing your visa application.

How to pay your fees - international students

Deposits and paying by instalments International students are required to pay a  deposit  prior to being issued a Confirmation of Acceptance for Studies (CAS). Your remaining balance will be paid in five monthly instalments over your first term. The first of these instalments must be paid when completing your enrolment on arrival at UEL. Please follow the payment instructions on our make a payment page . After the required payment has been made, you will be asked to complete the online International Student Reply Form to confirm your acceptance of our offer and of our terms of admittance and fees policy.

Our International team at UEL are available for advice and guidance on studying in London, fees, scholarships and visa requirements. Email:  [email protected]

Additional costs

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. Accommodation and living costs are not included in our fees. 

Our libraries are a valuable resource with an extensive collection of books and journals as well as first-class facilities and IT equipment. You may prefer to, or be required to, buy your own copy of key textbooks.

Computer equipment

There are open-access networked computers available across the University, plus laptops available to loan. You may find it useful to have your own PC, laptop or tablet which you can use around campus and in halls of residences.

Free WiFi is available on each of our campuses.

In the majority of cases, coursework can be submitted online. There may be instances when you will be required to submit work in a printed format. Printing and photocopying costs are not included in your tuition fees.

Travel costs are not included but we do have a free intersite bus service which links the campuses and halls of residence.

For this course, you will be:

• involved in processes of making, as a means of exploration, experimentation, and understanding your practice, by using a diverse range of media and materials
• required to purchase your own copy of books, for required reading
• required to produce physical artefacts for assessment 
• able to participate in optional study visits and/or field trips

However, over and above this you may incur extra costs associated with your studies, which you will need to plan for. 

In order to help you budget, the information below indicates what activities and materials are not covered by your tuition fees:

• personal laptops and other personal devices 
• personal copies of books 
• optional study visits and field trips (and any associated visa costs)
• printing costs
• your own chosen materials and equipment
• costs of participating in external events, exhibitions, performances etc.

The costs vary every year and with every student, according to the intentions for the type of work they wish to do. Attainment at assessment is not dependent upon the costs of materials chosen.

Learn about applying

Important information about your application, uk full-time starting sept.

How to Apply Apply directly to UEL by clicking on the apply button. For further information read our  Guide to Applying . When to Apply Places on many courses are limited and allocated on a first come first served basis. We advise you to apply as early as possible to give yourself the best chance of receiving an offer. Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone. +44 (0)20 8223 4354 Already applied? You can track the progress of your application by contacting our Applicant Engagement team on +44 (0)20 8223 3333 (Monday - Friday, 9am -5pm). Read our  guide to applying  for further information. Need help? Contact our Applicant Engagement team (Monday - Friday, 9am-5pm) +44 (0)20 8223 3333

UK Part-time starting Sept

How to Apply Apply directly to UEL by clicking on the apply button. For further information read our  Guide to Applying . When to Apply Places on many courses are limited and allocated on a first come first served basis. We advise you to apply as early as possible to give yourself the best chance of receiving an offer. Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone. +44 (0)20 8223 4354 Already applied? You can track the progress of your application by contacting our Applicant Engagement team on +44 (0)20 8223 3333 (Monday - Friday, 9am -5pm). Read our  guide to applying  for further information. Need help? Contact our applicant engagement team (Monday - Friday, 9am-5pm) +44 (0)20 8223 3333

International Full-time starting Sept

Submitting your application please read and consider the entry and visa requirements for this course before you submit your application. for more information please visit our  international student advice pages .  .

How to Apply We accept direct applications for international students. The easiest way to apply is directly to UEL by clicking on the red apply button. Please be sure to  watch our videos  on the application process.

When to Apply Please ensure that you refer to the international admissions deadline . We advise you to apply as early as possible to give yourself the best chance of receiving an offer.

International students who reside overseas Please ensure that you have read and considered the entry requirements for this course before you submit your application. Our enquiries team can provide advice if you are unsure if you are qualified for entry or have any other questions. Please be sure to read about the  Tier 4 visa requirements .

Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone.

+44 (0)20 8223 4354 Need help? Contact our applicant engagement team (Monday - Friday, 9am-5pm)

+44 (0)20 8223 3333

About our foundation years

Our Foundation Year courses are perfect for you if you... 

• are returning to education after a long time, or you don't have the qualifications for direct entry into our degree programmes
• are thinking of re-training and would like an introduction to the area
• are an international student wanting an additional year to adapt to the UK academic system
• are still evaluating which degree pathway at UEL is the right one for you

Please note: Foundation years can only be studied full time. However you can transfer to part-time delivery once you have completed your foundation year. Please apply to the full-time option if you wish to study in this way.

What makes this course different

Professional skill development

Block mode teaching, suitable for students in employment, allowing for professional skill development.

Enhanced knowledge

Integration of concepts, techniques and applications to enhance students' knowledge and skills in the analytics pipeline.

Open Source software tools

Open Source software tools which are widely used in the field of Data Science to extract value from data.

Course modules

Mental wealth; professional life (data ecology).

This module aims to develop a critical understanding of the world of data and Data Science from an ‘ecological’ perspective. This will focus on an understanding the environment of production, dissemination, harvesting and use of data in the data value chain as well as the development of niche areas from a perspective of evolution, competition, life cycle, cross-fertilisation and the niche space. This module focuses on many aspects of working in an Industry 4.0 economy.

Research Methods for Technologists

Applied research tools and techniques, work-based project review, planning for doctoral research, advanced decision making: predictive analytics & machine learning.

This module aims to develop a deep understanding of ways of making decisions that are based strongly on data and information. Particular focus will be on mathematical, statistical and algorithmic-based decision-making models using predictive analytics and machine learning. Various cases will be examined. The software environment will be predominantly open-source.

Spatial Data Analysis

This module aims for students to understand the concept and theory of spatial data analysis, and develop the skill and problem-solving ability by applying a range of spatial query, processing, visualisation and analysis techniques. Main platforms with be open source SpatiaLite and QGIS.

NOTE: Modules are subject to change. For those studying part time courses the modules may vary.

Download course specification

PDF, 185.2kb

What we're researching

Data analysis, data mining and modelling, Geocomputation and mapping, and data management. Professor Brimicombe is Emeritus Professor at UEL. He is a Chartered Geographer, an Academician of the Academy of Social Sciences, a Fellow of the Royal Statistical Society, a fellow of Royal Geographical Society, deputy chair of the National Statistician's Crime Statistics Advisory Committee and a non-executive committee member of the British Society of Criminology. He has been a Specialist Advisor to the House of Lords. Allan's expertise focuses around cross-disciplinary applications of Geo-Information Science and Data Science. Allan pioneered the use of geo-information systems and environmental simulation modelling. His other research interests include: data quality issues, spatial data mining and analysis, predictive analytics and location-based services (LBS). These have been applied to crime, health, education, natural hazards, utilities and business. Allan's recent projects include Olympic Games Impact Studies and Smart City Studies. Dr Yang Li is a fellow of the Royal Geographical Society, a fellow of the Royal Statistical Society, a fellow of the Higher Education Academy and a member of the Association of Geographic Information. Yang has rich experiences in both applications and research of Data Science and Geo-Information Science. He has expertise in data integration, data mining and data modelling. Particularly, he is a specialist in geocomputational analysis including data quality modelling and sensitivity analysis. Yang's recent projects include Olympic Games Impact Studies, the Prevent Project of the Home Office and TURaS.

Your future career

This programme uniquely qualifies students in a field increasingly recognised as central to most professional areas and research. The research component provides a solid grounding in methods and engagement with leading-edge ideas. Job opportunities in data science are rising exponentially. Holders of a Professional Doctorate in Data Science will have the highest possible qualification in this area and prepare them for senior positions. They will also be eligible to apply for Royal Statistical Society membership.

Our students are professionals from a diverse range of areas. They include a global compliance engineer, a senior system analyst, an analytical chemist, an assistant dean at Qatar University, a SAP technology consultant from Germany, an IT trainer, a senior project manager with Diageo, an ICT manager from Ireland, a lecturer in databases from Oman, a principal consultant with Verizon, a company MD, a senior analytical consultant with TripAdvisor, a consultant with HSBC,  a software developer with HMRC, a school teacher, a marketing officer,  a data manager in Microsoft and a data analyst from New York. 

All are looking to improve their career options and general expertise in this expanding market.

Explore the different career options you can pursue with this degree and see the median salaries of the sector on our  Career Coach portal .

How we support your career ambitions

We offer dedicated careers support, further opportunities to thrive, such as volunteering and industry networking. our courses are created in collaboration with employers and industry to ensure they accurately reflect the real-life practices of your future career and provide you with the essential skills needed. You can focus on building interpersonal skills through group work and benefit from our investment in the latest cutting edge technologies and facilities.

Career Zone

Our dedicated and award-winning team provide you with careers and employability resources, including:

• Online jobs board for internships, placements, graduate opportunities, flexible part-time work.
• Mentoring programmes for insight with industry experts 
• 1-2-1 career coaching services 
• Careers workshops and employer events 
• Learning pathways to gain new skills and industry insight

Mental Wealth programme

Our Professional Fitness and Mental Wealth programme which issues you with a Careers Passport to track the skills you’ve mastered. Some of these are externally validated by corporations like Amazon and Microsoft.

We are careers first

Our teaching methods and geographical location put us right up top

• Enterprise and Entrepreneurship support 
• We are ranked 6th for graduate start-ups 
• Networking and visits to leading organisations 
• Support in starting a new business, freelancing and self-employment 
• London on our doorstep

What you'll learn

Our doctoral research course focuses on pure or applied aspects of data science, with each student studying data from within their main discipline or area of employment. You will learn reflective and analytic approaches to data while engaging in your own data research.

The taught elements of the course include Data Ecology, Research Methods for Technologists, Applied Research Tools and Techniques, Spatial Data Analysis, Advanced Decision Making, Work-based Project Reviews and Planning for Doctoral Research.

These elements will be reinforced by the specialist knowledge of our course leaders, whose fields of expertise include data cleansing, data integration, data mining, spatial analysis and predictive analytics.

Their recent research has engaged them in data from crime statistics, natural hazards, public health and business, keeping them at the forefront of new developments in the field.

Our cross-disciplinary approach to the subject means that whatever your area of interest, our researchers will have the experience and expertise to enhance your knowledge and skills.

The taught modules on this course are available to be taken as credit-bearing short courses by suitably qualified individuals.

How you'll learn

This programme includes six taught modules and a Research Thesis and is available in full-time and part-time modes. Delivery of taught modules is by block and blended learning.

For those studying full-time, there are two years of research and for those studying part-time,  it is two years of taught modules and three  years of research.

Each taught module is based on one week's intensive attendance at the Docklands campus, according to an advertised calendar, usually at the beginning of each semester. Students are expected to have a laptop computer for in-class practical sessions. During the remainder of the semester, students can work on their reading, practical components (from a workbook) and coursework. Students will be supported online or on campus depending on individual students' arrangements. The taught modules on this programme are available to be taken as credit-bearing short courses by suitably qualified individuals.

How you will be assessed

All the learning outcomes of the programme are assessed through:

• Laboratory session portfolios
• Research thesis

Campus and facilities

Our campus and the surrounding area.

Our waterfront campus in the historic Royal Docks provides a modern, well-equipped learning environment.

Join us and you'll be able to make the most of our facilities including contemporary lecture theatres and seminar rooms, art studios and exhibition spaces, audio and visual labs and a multimedia production centre.

Features include our 24/7 Docklands library, our £21m SportsDock centre, a campus shop and bookstore, the Children's Garden Nursery, cafés, eateries, a late bar, plus Student Union facilities, including a student lounge.   University of East London is one of the few London universities to provide on campus accommodation. Our Docklands Campus Student Village houses close to 1,200 students from around the world. We are well connected to central London and London City Airport is just across the water. We also run a free bus service that connects Docklands with Stratford campuses.

Who teaches this course

This course is delivered by the School of Architecture, Computing and Engineering.

The teaching team includes qualified academics, practitioners and industry experts as guest speakers. Full details of the academics will be provided in the student handbook and module guides.

Related courses

This course is part of the Computer Science and Digital Technologies subject area.

Prof Doc Information Security

This programme aims to develop research-based practice amongst professionals currently working within the Information Security area.

Architecture, Computing and Engineering MPhil PhD

ACE has strong research expertise in urban sustainability, cyber-security and big data studies. We're world leaders in environmental protection studies.

TERMS AND CONDITIONS Modal

Terms of Admittance to the University of East London

The Terms of Admittance govern your contractual relationship with University of East London ("UEL"). A contract between you, the Student, and us, UEL, is entered into once you accept an offer of a place on a programme at UEL and this contract is subject to consumer protection legislation. You are entitled to cancel this contract within 14 days of enrolment onto your programme.

1) Student enrolment

Enrolment at UEL is the process whereby you officially become a UEL student. The enrolment process requires you to:

• Ensure that we are holding correct personal details for you
• Agree to abide by our regulations and policies
• Pay your tuition fees/confirm who is paying your tuition fees

You are expected to enrol by the first day of your academic year (click on "Discover") which will be notified to you in your enrolment instructions. Failure to enrol by the deadline contained in our Fees Policy (for most students by the end of the second week of teaching) may lead to the cancellation of student status and all rights attached to that status, including attendance and use of UEL's facilities. If you do not complete the formal process of enrolment but, by your actions, are deemed to be undertaking activities compatible with the status of an enrolled student, UEL will formally enrol you and charge the relevant tuition fee. Such activities would include attendance in classes, use of online learning materials, submission of work and frequent use of a student ID card to gain access to university buildings and facilities. Late enrolment charges may be applied if you do not complete your enrolment by the relevant deadline.

2) Tuition fees

Your tuition fee is determined by:

• the programme you are studying;
• if you are studying full or part-time;
• whether you are a UK/EU or International student; and when you started your studies with us.

We will tell you the tuition fee that you are due to pay when we send you an offer as well as confirming any additional costs that will be incurred, such as bench fees or exceptional overseas study trips. Unregulated tuition fees (where the UK government has not set a maximum fee to be charged) are generally charged annually and may increase each year you are on the programme. Any annual increase will be limited to a maximum of 5% of the previous year's fee. Regulated tuition fees (where the UK government has set a maximum fee to be charged) may also be subject to an annual increase. Any annual increase will be in line with the increase determined by the UK government. You will be notified of any increases in tuition fees at re-enrolment onto the programme. Further information on tuition fees and payment options are contained in our Fees Policy .

3) Student ID Cards

To produce an ID card, we need a recent photograph of you that is not obscured and is a true likeness. We will either ask you to send us/upload a photograph in advance of enrolment or take one of you at the point of enrolment. The photograph will be held on our student records system for identification purposes by administrative, academic and security/reception staff. By accepting these Terms of Admittance you are confirming that you agree to your photograph being used in this way. If you object to your photograph being used in this way please contact the University Secretary via email at gov&[email protected] . You are required to provide proof of your identity at initial enrolment and prior to the issue of your UEL student ID card. This is usually a full and valid passport but instead of this you may bring two of the following:

• A (full or provisional) driving licence showing current address
• An international driving licence
• An original birth certificate (in English)
• A debit or credit card (one only)
• A benefit book or benefit award letter (dated within the last 3 months)
• An Armed Forces Identity card
• A police warrant card

You are required to carry and display your student ID card whilst on UEL premises and must keep it safe so that it is not misused by others.

4) Proof of qualifications

You are required to produce evidence of having satisfied the entry requirements for your programme. Such evidence must be in the form of the original certificates or certified notification of results from the examining body. All qualifications must be in English or supported by an official certified translation. If you fail to provide evidence of having satisfied the requirements for the programme you are liable to be withdrawn from the programme.

5) Non-academic entry requirements

You may need to demonstrate that you have met non-academic entry requirements prior to enrolment by providing additional information to UEL. For example, if you:-

• are under 18 years of age at the time of initial enrolment,
• are applying to a programme that requires health clearance for study as stated in the programme specification,
• have declared a relevant criminal conviction,
• will be studying a programme that involves contact with children and/or vulnerable adults or leads to membership of a professional body that deals with children and/or vulnerable adults.

You will not be permitted to enrol and any offer will be withdrawn if UEL deems that you are unsuitable for study following assessment of this additional information in line with published policies. These policies will be provided to you when the additional information is requested.

6) Criminal convictions

UEL has a responsibility to safeguard staff, students and the wider community. You are required to inform UEL of any relevant criminal conviction you have and provide further information relating to these as requested. This includes any relevant criminal convictions received whilst studying at UEL. UEL will assess all information received in line with published policies and may remove you from a programme if the conviction makes you unsuitable for study in UEL's opinion. Failure to declare a relevant criminal conviction or provide further information about you may result in expulsion from UEL.

7) Providing false information to UEL

If you are discovered to have falsified or misrepresented information presented to UEL at application, enrolment or during your studies, you may be expelled from UEL.

8) Continued enrolment and student status

You are expected to abide by all UEL policies and regulations, both those in force at the time of first and subsequent enrolment and as later revised and published from time to time. UEL reserves the right to make reasonable changes to its policies and regulations and any substantial amendments will be brought to your attention. You are also required to take personal responsibility for your studies; this includes undertaking all study in support of your programme as prescribed by UEL. Key policies include: Manual of General Regulations This describes the general regulatory framework of UEL and gives information about how UEL confers its degrees, diplomas and certificates. It includes important information about academic performance requirements for continued study. Engagement Attendance Policy This outlines UEL's expectations of students in relation to attendance on and engagement with taught programmes. These students are expected to attend all scheduled classes and engage fully with learning materials and resources provided to them - failure to do so may result in withdrawal from module(s) and/or the programme. Code of Practice for Postgraduate Research Degrees The purpose of this code is to provide a framework for the successful organisation and implementation of good practice in all matters relating to postgraduate research degrees at UEL. It aims to ensure that all students are effectively supported and supervised so that the full scope and potential of their research is realised; that their thesis is submitted within regulatory periods and that they complete their programme with a suitable and sufficient portfolio of research and employment-related skills and competencies. Health and Safety Policy This describes the structures and processes by which UEL protects the health and safety of its staff, students and visitors. It confirms that students will receive sufficient information, instruction and induction in relation to health and safety. All students should take reasonable care for their health and safety. They must abide by UEL’s rules and regulations and co-operate with supervisors to enable them to fulfil their obligations. Students must not interfere intentionally, or recklessly misuse anything provided for health and safety. UEL has consulted with its students and staff and has adopted a No Smoking Policy to safeguard the health and well-being of its community. Students are required to comply with this policy which restricts smoking to designated shelters and prohibits the use of electronic cigarettes within any UEL building or near building entrances. For further information on our Healthy Campus initiatives and support please visit the Health and Safety pages . Student Disciplinary Regulations and Procedures (incorporating the student code of conduct) This code is more than a list of things that we should and should not do: it reminds us that we should always consider how our behaviour affects others. The code applies:

• to all students;
• at all sites throughout our estate, and;
• when we represent UEL on business beyond our campus, both in real (face-to-face) and virtual environments.

And outlines expectations of students:

• verbal and physical behaviour should always be polite and respectful;
• behaviour should not impair the engagement, learning or participation of others;
• anti- social behaviour by individuals and groups will not be tolerated.

9) Changes to scheduled programmes

UEL will take all reasonable steps to ensure that the programme of study that you have accepted will conform to the programme specification published on our website and will ensure that the necessary resources required to enable you to meet the required learning outcomes and pass the relevant assessments are available. In order to ensure that our programmes are current and relevant, they are subject to regular review. From time to time, to ensure the maintenance of academic standards and/or compliance with professional body requirements, it may be necessary to amend a module or make adjustments to programme content. Major changes to programmes that in the reasonable opinion of UEL, will have a significant impact on students will involve consultation with students already enrolled on the programme when the changes are proposed. Once any changes are confirmed, UEL will notify all students and applicants of the changes. When UEL reasonably considers that the change may only impact one or more cohorts on the relevant programme, UEL may decide to only consult with the relevant cohort. In the event that we discontinue a programme, we will normally permit existing students to complete the programme within the typical duration of study. In these circumstances, UEL will use reasonable endeavours to continue the programme for existing students without making major changes. If this is not possible, we will support students in changing to another UEL programme on which a place is available, and for which the student is suitably qualified, or assist with transfer to another HEI to complete the programme elsewhere.

10) Changes to these terms

We may change these terms from time to time where, in UEL's opinion, it will assist in the proper delivery of any programme of study or in order to:- (a) Comply with any changes in relevant laws and regulatory requirements; (b) Implement legal advice, national guidance or good practice; (c) Provide for new or improved delivery of any programme of study; (d) Reflect market practice; (e) In our opinion make them clearer or more favourable to you; (f) Rectify any error or mistake; or (g) Incorporate existing arrangements or practice. No variation or amendment to these Terms of Admittance may be made without our prior written agreement. In the event that we agree to transfer you to an alternative programme of study, the transfer will be considered to be a variation to the Terms of Admittance, which shall otherwise remain in full force and existence. If we revise the Terms of Admittance, we will publish the amended Terms of Admittance by such means as we consider reasonably appropriate.;We will use reasonable endeavours to give you notice of any changes before they take effect.

11) Data Protection

UEL is committed to adhering to its obligations under the Data Protection Act 2018 and will act as a Data Controller when it processes your personal data. You can find our registration to the Data controller register on ico.org.uk . UEL processes your personal data fulfil its contractual and legal obligations to students. Personal data that we process about you includes:

• Your contact details and other information submitted during the application and enrolment processes;
• Details of courses, modules, timetables and room bookings, assessment marks and examinations related to your study;
• Financial and personal information collected for the purposes of administering fees and charges, loans, grants, scholarships and hardship funds;
• Photographs, and video recordings for the purpose of recording lectures, student assessment and examinations and for the purposes of university promotion that is in our legitimate interest but still fair to you;
• Information about your engagement with the University such as attendance data and use of electronic services such as Moodle, Civitas and YourTutor;
• Contact details for next of kin to be used in an emergency;
• Details of those with looked after status or those who have left the care system for the provision of support;
• Information related to the prevention and detection of crime and the safety and security of staff and students, including, but not limited to, CCTV recording and data relating to breaches of University regulations;

This is not an exhaustive list, for further information please refer to our fair processing notice pages on uel.ac.uk. In all of its data processing activities, UEL is committed to ensuring that the personal data it collects stores and uses will be processing in line with the data protection principles which can be summarised as:

• Being processed lawfully, fairly and in a transparent manner;
• Collected for specified, explicit and legitimate purposes;
• Adequate, relevant and limited to what is necessary;
• Accurate and, where necessary, kept up to date;
• Kept in a form which permits identification of data subjects for no longer than is necessary;
• Processed in a manner that ensures appropriate security of the personal information;
• Be accountable for, and be able to demonstrate compliance with, the six principles above.

Student Responsibilities You must ensure that:

• All personal data provided to UEL is accurate and up-to-date. You must ensure that changes of address etc. are notified to the Student Hub.
• Students who use UEL's computing facilities may process personal data as part of their studies. If the processing of personal data takes place, students must take responsibility for that processing activity to ensure that it in line with the data protection principles above.
• Students who are undertaking research projects using personal data must ensure that:
• The research subject is informed of the nature of the research and is given a copy of UEL's Fair Processing Notice and this Data Protection Policy.

12) Legal basis for use of data

By agreeing to these Terms of Admittance and enrolling at UEL, you are agreeing to the terms and conditions of a contract for the use of your personal data relating to your enrolment, and if appropriate, registration and ongoing participation on a programme of study. Your personal or special category data will be collected, processed, published and used by UEL, its online learning and teaching services and/or its partners and agents in ways which support the effective management of UEL and your programme of study, to allow for the delivery of bursary schemes and to support improvements to student experience and progression, and are consistent with: The terms of the Data Protection Act 2018; Any notification submitted to the Information Commissioner in accordance with this legislation; and compliance with any other relevant legislation. You have fundamental rights associated with how organisations use your personal data. Further information on data protection and use of your personal data can be found in our Data Protection Policy and on uel.ac.uk.

13) Intellectual property

You are entitled to the intellectual property rights created during your time studying at UEL that would belong to you under the applicable law. There are some programmes where the assignment of certain types of intellectual property to UEL is appropriate. UEL will require the assignment to it of intellectual property rights relating to postgraduate research that is part of an ongoing research programme. Where the nature of the research programme means that some assignment of intellectual property rights to UEL is appropriate, we will take what steps that we can to ensure that your interests are protected. UEL will take reasonable endeavours to ensure:-

• the scope of the assignment is narrow, and is restricted to what is necessary, for example to protect UEL’s legitimate interests in the intellectual property created as party to a research programme;
• the application of the assignment is clearly defined, so that it is clear to you in which circumstances the assignment will apply;
• where the assignment of the intellectual property is appropriate in the circumstances, we will take all reasonable steps to ensure that the rights of the parties are evenly balanced (for example, your work being acknowledged in a publication and, where appropriate, subject to an appropriate revenue sharing scheme)
• where UEL claims ownership of intellectual property rights in relation to a taught programme of study, such treatment of those rights will be made clear in the published information relating to that programme.

14) How we communicate with you

UEL will communicate with you via a variety of channels, including postal letter, e-mail, SMS text message and online notices. To enable this, we request that you provide us with your e-mail address, postal address, and contact telephone number when you first enrol. Throughout your studies, it is important that you keep your contact details up to date. You can view and edit this information by logging into our student portal, UEL Direct at https://uel.ac.uk/Direct . We will create a UEL e-mail account for you after you enrol. Your e-mail address will be your student number, prefixed with a ‘u’ and followed by ‘@uel.ac.uk’ – e.g.: [email protected]. UEL will use this e-mail address to communicate with you and it is important that you regularly check and manage this mailbox for important updates and information. You can access your email account, plus information about our services, news and events by logging into our Intranet, intranet.uel.ac.uk. At the login screen, enter your email address (as above) and password. Your default UEL password will be your date of birth, formulated as DD-MMM-YY, e.g. 31-jan-84. Your UEL email account and associated UEL IT accounts will be deleted not more than 6 months after you graduate or withdraw from your programme of study (if earlier).  

15)University of East London Students' Union

The University of East London Students' Union (UELSU) represents students at UEL. By enrolling at UEL you are automatically granted membership of both UELSU and the National Union of Students (NUS). If you wish to opt out from this membership, please inform UELSU in writing at either [email protected]  or by writing to: Chief Executive, UELSU, University of East London, Docklands Campus, 4-6 University Way, London E16 2RD. UELSU provides a range of services and support to students and can provide advice and representation on any matter affecting the contract between you and UEL. For further information on this support, please visit www.uelunion.org

16) Students studying at partner institutions

If you are undertaking a programme of study at a partner institution you will need to generally abide by the above terms and also those of the partner institution. Further information and support in understanding these terms is available from the Academic Partnership Office -  [email protected] .

17) International students - additional responsibilities

All international students must also comply with UK Visa and Immigration requirements. All international students are required to hold a valid visa which permits study in the UK or hold a Tier 4 visa/have applied for a Tier 4 visa with a Confirmation of Acceptance for Studies issued by UEL. Students who are being sponsored under a Tier 4 student visa must also understand and comply with the responsibilities of their student visa and co-operate with UEL in fulfilling our Tier 4 duties .

18) Equality, Diversity and Inclusion

UEL is committed to working together to build a learning community founded on equality of opportunity – a learning community which celebrates the rich diversity of our student and staff populations and one in which discriminatory behaviour is challenged and not tolerated within our community. Within the spirit of respecting difference, our equality and diversity policies promise fair treatment and equality of opportunity for all regardless of gender, ethnicity, sexual orientation, age, disability or religion/belief (or lack of). In pursuing this aim, we want our community to value and to be at ease with its own diversity and to reflect the needs of the wider community within which we operate. For further information on this inclusive approach to education please visit our Student Policies page .

19) Complaints

We welcome feedback on our programmes and services and facilitate this in a variety of ways, including programme committees, module evaluation forms and surveys. However, if you are dissatisfied with a particular service or programme or the manner in which it has been delivered, you must let the person responsible for that service know as we will always try to resolve matters at the earliest opportunity via informal conciliation. If you are unsure who to approach, please e-mail The Hub who will be able to direct your concerns appropriately. If you remain dissatisfied with a service or programme, or the manner in which it is delivered, you should refer to our formal complaints procedure to have the matter formally addressed. In addition, once you have enrolled onto your programme, you will also have access to the Advice and Information Service offered by UELSU. This access is not available to students studying at partner institutions.

20) Cancellation

If you wish to cancel this contract within 14 days of enrolment onto your programme, you must do so in writing. Any fees that you have paid will be refunded – please see Fees Policy for further information on obtaining a refund.

21) Further guidance

If any of the information in these Terms of Admittance or related policies are unclear or if you have any questions, please contact The Hub for guidance on +44 (0) 208 223 4444 .

22) Right to advice

This is a consumer contract and you are able to obtain independent advice in relation to its terms and conditions from UELSU as well as your local Citizens Advice Bureau.  

23) General

Neither you nor UEL will be liable for failure to perform their obligations under these Terms of Admittance if such failure arises from unforeseeable events, circumstances or causes outside of that party's reasonable control. Examples of such events include, but are not limited to, war, terrorism, industrial disputes, natural disaster, fire and national emergencies. Only you and UEL are parties to these Terms of Admittance. No other person shall have any rights under the Contracts (Rights of Third Parties) Act 1999 to enforce any term of these Terms of Admittance. Failure or delay by you or UEL to exercise any right or remedy provided under this contract shall not constitute a waiver of that or any other right or remedy, nor shall it prevent or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall prevent or restrict the further exercise of that or any other right or remedy. These Terms of Admittance are governed by the law of England and Wales and you and UEL agree to submit to the exclusive jurisdiction of the courts of England and Wales.

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MSc Data Science

Page contents

• 1 Introduction
• 2 Key features
• 3 Teaching Centre Support
• 4 Course overview
• 5 Key dates
• 6 Admissions
• 7 Fees, funding and payment
• 8 Career opportunities

You are reading:

Course information>

October 2024

1*-5 years (*depending on module availability)

Learn how to apply technology to real-world data science problems and gain an in-depth understanding of emerging technologies, statistical analysis and computational techniques with this flexible master's degree in data science.

Key features

In-demand digital skills and knowledge.

Learn how to apply technology to real-world data science problems and gain an in-depth understanding of statistical analysis and computational techniques. Acquire transferable skills that will help advance your career.

Specialise your degree

You have the option to study one of two specialist pathways. The Artificial Intelligence pathway may open up career opportunities in technology firms, robotics, military, academia, and public research sector, while Financial Technology can help you get a job in the financial sector.

A mark of excellence

You’ll gain a prestigious qualification, respected by employers worldwide. The degree has been developed by Goldsmiths, University of London, one of the UK’s top institutions for innovation and creativity.

Study online anywhere in the world

Fit your studies around your commitments and pursue an internationally recognised degree without putting your life on hold. Continue to build your career momentum while gaining the knowledge and skills to unlock future opportunities. Benefit from comprehensive study materials written specifically for the degrees by leading experts.

Unlock a wealth of study resources

Access interactive computer sessions, study guides, past examination papers and more via the Virtual Learning Environment (VLE). Receive personalised assignment feedback, tutorial support and discuss course material with other students through the online discussion forums.

Teaching Centre Support

Course overview, programme structure, modules and specification show.

The degree is available to be studied as a full master’s degree, a Postgraduate Diploma (PGDip) or a Postgraduate Certificate (PGCert).

Individual modules: There is provision for individual modules to be studied and assessed on a stand-alone basis without being registered for a related qualification. You may register for any number of core or optional modules on a stand-alone basis (subject to module availability), with the exception of the Final Project.

View MSc Data Science module release dates [PDF]

You can also choose from one of two specialist pathways in:

Artificial Intelligence: MSc Artificial Intelligence | PGDip Artificial Intelligence

Financial Technology: MSc Financial Technology | PGDip Financial Technology - modules

The Programme Specification and Programme Regulations contain information and rules regarding what courses you can choose and the order in which they must be studied.

• Download the Programme Specification MSc
• View Programme Regulations

MSc Data Science Show

MSc: Four core modules, two compulsory modules, four optional modules, plus a Final Project (180 credits).

Postgraduate Diploma: Four core modules, two compulsory modules and two optional modules (120 credits)

PGCert: Two core modules and two optional modules (60 credits).

View MSc Data Science module release dates .

Core modules

Statistics and Statistical Data Mining (Open modal with additional information)

Machine Learning (Open modal with additional information)

Data Programming in Python (Open modal with additional information)

Big Data Analysis (Open modal with additional information)

Compulsory modules

Data Visualisation (Open modal with additional information)

Data Science Research Topics (Open modal with additional information)

Optional modules

Natural Language Processing (Open modal with additional information)

Social Media and Network Science (Open modal with additional information)

Artificial Intelligence (Open modal with additional information)

R for Data Science (Open modal with additional information)

Neural Networks (Open modal with additional information)

Blockchain Programming (Open modal with additional information)

Financial Data Modelling (Open modal with additional information)

Financial markets (Open modal with additional information)

Mathematics for Data Science (Open modal with additional information)

Final Project (MSc only)

Final Project (MSc) (Open modal with additional information)

MSc Artificial Intelligence pathway Show

MSc: Four core modules, three compulsory modules, three optional modules, plus a Final Project.

Three compulsory modules

Three optional modules, pgdip artificial intelligence pathway show.

PGDip: Four core modules, three compulsory modules, plus one optional module.

One optional module

Msc financial technology pathway show, four core modules, pgdip financial technology pathway show, plus one optional module, how you study show.

You can study this online degree from anywhere in the world. The flexible approach to learning enables you to fit your studies around your commitments whilst providing the academic rigour and structure of an on-campus programme.

Modules are offered over two 22-week sessions each academic year. You choose which sessions to enter and how many modules to take in each session.

Assessment deadlines are outlined clearly in advance of the session.

• The maximum number of modules you can study in one session is six, (or four plus the final project). You will also receive comprehensive learning materials and support from online tutors.

Study materials

We provide you with all of the resources and study materials you need to complete the course successfully, including the essential reading for each module. You can access these through the Virtual Learning Environment (VLE) on a range of devices.

Our online learning resources typically include multimedia content, activities and exercises (e.g. multiple choice quizzes, reflective exercises and self-assessment questions), as well as facilities for you to interact with your tutor and fellow students.

When you register with us, you will gain access to all resources and study materials via your Student Virtual Learning Environment (VLE), that will equip you to complete each module successfully. You will gain access to a range of multimedia content, activities, and exercises, as well as the opportunity to engage with your online tutor and fellow students.  

Online Library  

As a student at the University of London, you will have access to a range of resources, databases, and journals via the  Online Library . You will be able to contact a team of professional and qualified librarians for any help you require.

If you’re based in the United Kingdom, or are visiting London, make sure to visit  Senate House Library . Students studying with the University of London can join the library free of charge. Membership includes a 10-book borrowing allowance, access to all reading rooms and study areas, and on-site access to Senate House Library digital resources.

Online tutor support

Studying our online MSc Data Science entitles you to receive tutor support and feedback. You will join an online tutor group to receive academic support and guidance on assessments. If you choose to study as a web-supported learner, you will have the opportunity to join an online tutor group and to engage with your fellow students. If you are interested in studying with a  local teaching centre , you can benefit from face-to-face tuition.

All students receive tutor support and feedback while studying this programme. Tutors introduce the modules, respond to queries, monitor discussions and provide guidance on assessments.

Web-supported learning: if you register for a module as a web-supported learner, you join an online tutor group.

Institution-supported learning: if you enrol for a module with a local teaching centre, you receive face-to-face tuition. We work with several teaching centres in a number of countries and will recruit more to support the programme.

Student Support

We are committed to delivering an exceptional student experience for all of our students, regardless of which of our programmes you are studying and whether you are studying independently or with a Recognised Teaching Centre.

You will have access to support through:

• The Student Advice Centre – provides support for application and Student Portal queries.
• TalkCampus – a peer support service that offers a safe and confidential way to talk about whatever is on your mind at any time of day or night.

Time commitment

Study at your own pace, either part-time or full-time. Once you begin a module it is generally expected that you will complete it in the six-month session. Each module presents about 150 hours of study. Over a 22-week session, a 15 credit module will typically require five to seven hours of work/effort per week, and a 30 credit module will typically require ten to 15 hours of work/effort per week.

Each module includes a mix of assessments. During your study period you will undertake formative assessments, which help you to measure your progress but do not count towards your grade, and summative assessments Summative assessments do count towards the final grade. These include a mid-session coursework submission and an unseen written examination (or final project) at the end of the session.

Written examinations are held twice a year. You can defer sitting an exam once (subject to a fee) but you cannot defer the submission of coursework.

More about exams

Academic Leadership Show

The academic content for the postgraduate Data Science degrees has been developed by the University of London with academic direction by the Department of Computing at Goldsmiths, University of London, one of the UK’s top creative universities.

Goldsmiths' unique hands-on project-based style works for a diverse range of interests – from computer and data science to art and music to social science and journalism.

Programme Director

Dr Tim Blackwell is a senior lecturer in Computer Science at Goldsmiths, University of London. Prior to his post at Goldsmiths, Tim was with the Open University, Edinburgh and Glasgow Universities and Imperial College, London. He trained as a theoretical physicist and computer scientist and researches a wide portfolio of subjects. Tim is best known for the creation of Swarm Music, an autonomous computer improviser. Much of his current work focuses on swarm intelligence algorithms and their use in problem solving. For example, he is currently researching swarm intelligent reconstructions of medical imaging acquisitions.

Tim is passionate about online and distance learning, and continuing education. He has delivered courses in a wide variety of subjects ranging from Quantum Philosophy to the Music of John Coltrane. Whilst at Goldsmiths he has led computer science and music computing modules across all undergraduate and postgraduate levels. In particular, he is leader of the Artificial Intelligence and Neural Networks modules.

Tim recently assumed the role of director of the MSc Data Science degree, which benefits from the input of Goldsmiths’ data science researchers, endeavours to deliver the essential cutting-edge and industry-standard techniques of this increasingly relevant discipline.

April 2024 intake Show

October 2024 intake show, entry requirements show.

We offer two entry routes into the degrees, so if you do not meet the academic requirements you may still be eligible to apply through an alternative route.

Entry Route One (MSc/PGDip/PGCert) and individual modules

To be eligible to register for any of the Data Science degrees, you must have the following:

• A bachelor’s degree (or an acceptable equivalent) in a relevant subject which is considered at least comparable to a UK second class honours degree, from an institution acceptable to the University.
• Previous degrees should normally include a sufficient level of programming such as Python detailed in your transcript. Whilst other degrees such as Engineering and Mathematics will be considered on a case by case basis.
• If we consider your previous degree as non-relevant then we will request you take our MOOC, Foundations of Data Science: K-means Clustering in Python , before you start our Data Science programme. This MOOC requires approximately 30 hours of study.

Entry Route Two (MSc/PGDip/PGCert) and individual modules

A bachelor’s degree (or an acceptable equivalent) in any subject which is considered at least comparable to a UK second class honours degree, from an institution acceptable to the University.

• In addition to the above, you will be required to complete an online preparatory course prior to registration. The online preparatory course, Foundations of Data Science: K-Means Clustering in Python , requires approximately 30 hours of study.

English Language requirements

You need a high standard of English to study this programme. You will meet our language requirements if you have achieved one of the following within the past three years:

• IELTS: at least 6.5 overall, with 6.0 in the written test.
• TOEFL iBT: at least 92 overall, with 22+ in reading and writing and 20+ in speaking and listening.
• Cambridge Certificate of Proficiency in English.
• Cambridge Certificate of Advanced English (at grade C or above)
• Duolingo: must achieve an overall score of at least 120.

Alternatively, you may satisfy the language requirements if you have at least 18 months of education or work experience conducted in English.

As this is a technical degree, you will need regular access to a computer with an internet connection and a minimum screen resolution of 1024x768. You will also need to view video material and have a media player (such as VLC) to play video files.

More about computer requirements

Recognition of prior learning Show

If you have studied material as part of a previous qualification that is comparable in content, level and standard to our Data Science modules, you may be exempted from the equivalent course of our degree. This is known as Recognition of Prior Learning (RPL) or Exemption. You will not need to study or be assessed in the module(s) to complete your award.

If you are registering on the following qualifications, you may be awarded RPL up to:

• MSc: 120 UK credits
• PGDip: 60 UK credits
• PGCert: 30 UK credits

RPL for the Final Project will not be considered.

To be considered for RPL you should make a formal request within your application when applying for the programme. Or, you can submit an online enquiry , if you have already applied.

You will need to have met the entrance requirements for the programme to be considered for RPL.

You must have completed the qualification/ examination(s), on which the application for RPL is based on, within the five years preceding the application.

We will not recognise or accredit prior learning for a module later than 14 days after the module start date. You will be deemed to have started a module once you have been given access to the learning materials on the VLE.

Some qualifications are automatically recognised as meeting the learning outcomes of our courses. If you satisfy the conditions, we will accredit your prior learning as detailed here: Recognition of Prior Learning degrees in Data Science . No fees are charged for this service.

With the exception of the qualifications noted in the automatic RPL section on our website, applications for RPL based on examinations from professional institutions or professional certificates will not normally be considered.

Discretionary

Other qualifications will need to be assessed by specialist academics on a case by case basis , before we can approve RPL. A formal application is required and an RPL application fee is payable. The RPL application fee is non-refundable, even if your prior learning is not recognised.

Your qualification must be at the appropriate level (usually equivalent to a UK Level 7/ Master’s degree qualification or above) to be considered.

For your discretionary RPL request to be processed, you will need to provide : a completed RPL request form, the supporting documentary evidence (normally a scanned copy of an official transcript and syllabus of your previous studies) and the discretionary RPL fee.

You should apply as soon as possible so that we can process your request. You will need to allow time for academics to consider your documentation, so you can register by the registration deadline. 

All discretionary RPL requests must be submitted by the dates specified for the April or October session, in the year that you apply. We must receive all required supporting evidence by the deadline stated. 

If you submit your discretionary RPL application but are too late to be considered for RPL in the current session, we will still process your application to study the programme. If you receive an offer, you can still register. If you wish to be considered for RPL in a subsequent session, then you shouldn’t register on the modules you want to apply for RPL.

How to request RPL

Additional Information about the process of applying for RPL can be found here .

Further information regarding RPL is covered in the Recognition of Prior Learning section of the appropriate Programme Regulations and Section 3 of the General Regulations.

Fees, funding and payment

The fee depends on two factors:

• Whether you choose web-supported or Recognised Teaching Centre supported learning.
• Whether you live in a developing (Band A) or developed (Band B) nation. See the list of Band A and Band B countries [PDF]

Important: the table below does not include fees payable to a third party, such as tuition costs payable to a Recognised Teaching Centre or fees charged by your local examination centre, or local VAT, Goods or Services Tax (GST) or sales tax.

The fees below relate to new students registering for the 2023-2024 session. On average, fees are subject to a five per cent year-on-year increase.

Students who registered earlier can view their fees on the Course Fees page .

Disclaimer: Currency conversion tool

*The indicative totals given represent the amount you would expect to pay if you were to complete the MSc degree / PGDip / PGCert in the minimum period of time (one year, subject to module availability), without resits, and with a year-on-year increase of five per cent. These totals do not reflect the cost of any additional tuition support you may choose to take or the fee levied by your local examination centre.

*The online examination administration fee is charged for each examination paper held online, including resits. This does not apply to any coursework submissions.

How fees work

Your fees include study materials and entry into assessments.

The indicative programme fee includes all module and continuation fees for the duration of your study, as well as online tutor support.

With pay per module , you pay for each module as you register for it. The 'web-supported learning' fee includes support from a University of London online tutor. Alternatively, if you prefer face-to-face tuition, you can pay a smaller fee to us and a separate fee to a teaching centre which supports the programme.

The module continuation fee is the cost per module if you defer an examination or need to retake assessments. It includes all study materials, entry into assessments, and online tutor support.

Additional costs

You will also need to budget for:

• Exams: our approved examination centres around the world charge a fee when you sit an exam. Contact your chosen examination centre for details about costs.
• Tuition: as described, teaching centres charge face-to-face tuition if you choose to take modules with institution-supported learning.
• Recognition of prior learning applications: these are not included with the course fees.

Some fees are non-refundable. Please see the refund and compensation policy for further details.

Please note: All student fees are net of any local VAT, Goods and Services Tax (GST) or any other sales tax payable by the student in their country of residence. Where the University is required to add VAT, GST or any other sales tax at the local statutory rate, this will be added to the fees shown during the payment process. For students resident in the UK, our fees are exempt from VAT.

Further information on Sales Tax.

Funding your study Show

Without the cost of moving to London, studying for your University of London degree anywhere in the world represents excellent value for money. However, there are additional sources of support depending on where you live and how you choose to study.

If you are a UK or EU national and you have lived in England for three years, you could be eligible to apply for a Postgraduate Loan.

More on funding your study.

Scholarships

The Aziz Foundation Scholarship Programme offers four Master’s scholarships for the 2023-24 academic year.

Can I get sponsored?

If you're employed, your employer may be willing to cover part/all of the programme fees if you can make a compelling case as to how this programme will boost your contribution to the workplace.

Our courses are ideal for employers because they get to retain you as an employee and benefit from your learning from the moment you begin.

How can I get sponsored by my employer?

Paying for your course Show

You can pay your fees in a number of ways, including an online payment facility via the Student Portal and Western Union Quick Pay.

More on how to pay your fees

Career opportunities

Careers opportunity show.

Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government.

Our flexible degree addresses the skills shortage of data scientists who can use data to drive improvements to organisational performance. You will have the opportunity to gain highly-valued skills through the specialist pathways:

MSc Data Science These skills will lead to a variety of careers with employers from technology firms, the biomedical research sector, the charitable and voluntary sector, and public research sector.

MSc Data Science and Artificial Intelligence Embark on a variety of careers with employers from leading technology firms, robotics, military, academia, and public research sector.

MSc Data Science and Financial Technology For a variety of careers with employers from the financial sector, including financial planning, insurance, marketing, and investment banking.

What do employers think of our graduates?

In some countries, qualifications earned by distance and flexible learning may not be recognised by certain authorities or regulators for the purposes of public sector employment or further study. We advise you to explore the local recognition status before you register, even if you plan to receive support from a local teaching centre.

Careers support Show

You’ll have access to a wide range of careers and employability support through the University of London Careers Service, including live webinars and online drop-in sessions.

More on the University of London Careers Service

Tailored support for careers in the refugee and humanitarian fields is available through regular programme events, webinars and careers resources.

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Related content, goldsmiths, university of london.

MSc Public Health

MSc Global Health Policy

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• Degrees and awards
• Digital and data skills

Data Science

Study data science intensively online, whatever your graduate field, and meet the rapidly growing demand for data scientists in the workforce.

Key info - MSc

This indicative cost is based on 180 credits of study over two years, starting in September 2024.

Join our intensive online MSc in Data Science.

Recent advances in data science have been seismic. Big data, predictive analytics, and AI technologies like ChatGPT and Bard AI are revolutionising the way organisations automate processes, predict trends, and engage with customers.

As the volume, diversity and complexity of data being gathered continues to grow, the big challenge facing organisations today is how to analyse this data and use it to inform business decisions.

Join our online Masters in Data Science and you’ll learn to do just that.

Why this online data science degree is different

You’ll go beyond classical statistics and big data in this degree.

You’ll set yourself apart by gaining additional skills in computational thinking (CT), programming, sequencing and algorithms, allowing you to gain better insights from data and create solutions to problems across disciplines and industries.

Who can join our online MSc in Data Science?

You do not need to have a background in maths or programming to join our online MSc.

Its multidisciplinary and multisector focus means we welcome applications from a wide range of academic and professional backgrounds, including the humanities, business and social sciences, as well as science, technology, engineering and medicine.

Aligned with industry

We’ve designed this degree in collaboration with industry partners, including Wolfram Research, to ensure you cover the algorithms, tools and workflows required by industry.

You’ll learn to use Wolfram Mathematica and programming fundamentals that underpin languages including Python and R.

By the end of this degree, you’ll be able to...

• Utilise the algorithms, tools and workflows used at the forefront of data science today, including Wolfram Mathematica.
• Apply analytic and computational thinking skills to extract knowledge from data.
• Work effectively in multidisciplinary teams to extract knowledge and insights from data.
• Combine mathematical modelling and programming skills in the context of data science.
• Produce meaningful data insights that organisations can use to improve their performance.

Why study data science online with the University of Aberdeen?

Earn a respected Masters degree

You’ll earn exactly the same globally recognised Masters degree online as you would on campus.

Over 525 years of excellence

Graduate from the fifth-oldest university in the English-speaking world, founded in 1495.

20% alumni discount

University of Aberdeen alumni receive 20% off fees for this online degree.

What you’ll study

You’ll study the following courses that make up the 180 credits of this Masters degree.

You can also choose to exit early, earning a:

• Postgraduate Diploma (120 credits), or
• Postgraduate Certificate (60 credits).

How you’ll study

• Online learning

Our distance-learning MSc Data Science is delivered 100% online. You can study with us anywhere in the world, with no need for a study visa.

Vary the pace

Each course that’s part of this degree is taught intensively (around 40 hours per week) for three weeks.

You can choose to study one to four courses per term, to vary the pace of the degree to suit you.

Your teaching

Your teaching is delivered through MyAberdeen, our online Virtual Learning Environment (VLE). It holds all the materials, tools and support you’ll need in your studies.  Take a look around MyAberdeen .

You can access your learning materials on computer, smartphone and laptop, 24 hours a day. You’ll find a range of resources at your fingertips, including online access to our award-winning  Sir Duncan Rice Library . 

Your tutors

You’ll learn from a team of academic experts from our School of Natural and Computing Sciences , who work in data science and mathematical modelling.

Industry input

We have strong links to industry and developed this degree in collaboration with industry partners, including Wolfram Research. This ensures you’ll cover the algorithms, tools and workflows needed by industry today.

Assessments

MSc Data Science is assessed entirely online. You’ll have a range of assessment methods throughout your degree, including online tests, essays and projects.

Study hours

Intensive hours for taught courses.

Each 15-credit course that is part of this degree is taught intensively (around 40 hours per week) for three weeks.

This means you need to be able to dedicate full-time study hours to each course you take.

Course hours breakdown

Each course:

• is taught for three weeks, followed by an assessment
• totals around 150 hours of study and assessment time
• takes around 40 hours per week to complete, including preparing for assessments.

This is an indicative guide to the time required for a typical student at this level to achieve the learning outcomes.

Courses are delivered one at a time, consecutively.

You can choose to take one to four courses per term to vary the pace. A term is usually 12 weeks long.

Activities at fixed times

There will be some activities that are scheduled at fixed times, such as online meetings with your tutor, or assessments with deadlines.

You can to an extent set your own study hours each week to cover the materials. MyAberdeen is available 24/7, so you can log in and study around the clock.

Hours for 60-credit project

A 60-credit project is around 600 hours of study time. This is around one term of full-time study.

You can dedicate a full term to your project and work on it full-time. Or you can complete it part-time, spreading the hours you dedicate to it over two or more terms.

Study experience

When you study with us, you can expect a first-class support structure so that you’re never alone in your studies.

But learning online does mean you have to motivate yourself and manage your own time.

Your most important commitment will be time – the time to work through, reflect on and understand your teaching materials.

Before you start a course that involves a high degree of independent study, we recommend looking at the time you will be able to devote to your studies each week:

• Be realistic
• Create a weekly schedule as a guide

If you have any questions about studying online, get in touch with our friendly team . We’re here to help.

Our first-class support structure will ensure that you aren’t alone in your studies. You’ll have contact with your tutors via MyAberdeen and email. You can use social media and discussion boards to chat with your fellow students too.

We provide a wide range of services to support you in your studies and beyond:

• Careers and Employability Service – including one-to-one advice sessions
• Disability support
• Library support
• Student Support Service – help with finances, stress, wellbeing and non-academic issues
• Student Learning Service – study support, with advice sessions available via phone or Skype
• Aberdeen University Students’ Association (AUSA) – run by students for students
• Toolkit – clever apps and free training that can make your study life easier

Wherever you are in the world, you’ll feel part of our very special Aberdeen learning community.

Use your local university libraries

We’re a member of the Access scheme run by the Society of College, National and University Libraries (SCONUL).

Access study spaces, books and journals in your area

The SCONUL Access scheme allows you, as a University of Aberdeen student, to access books and resources at university libraries across the UK and Ireland, or visit them for a quiet place to study.

You’ll be able to use study spaces, books and journals at over 150 university libraries which belong to the scheme.

Find out about the SCONUL Access scheme for libraries .

Your support team

Our friendly team are here to answer any queries you have before, during and after your studies.

Professor Marco Thiel

Marco is the degree coordinator. He’ll be on hand to answer any questions about degree content before you start and to help you throughout your studies.

Rowena Hardy

Rowena is part of the School’s postgraduate teaching support team. She’ll be there throughout your studies to answer any of your non-academic queries.

Get in touch

The online education team is here to answer any questions you have right now about this qualification, or about studying online.

Where this will take you

Your msc qualification.

You’ll graduate with an MSc in Data Science from the University of Aberdeen.

You’ll receive exactly the same degree qualification as you would on campus. Your degree title will not mention online.

Your qualification will be recognised by industry and educational institutions around the world, opening up international opportunities.

There are great graduate prospects in the field of Data Science. The demand for data scientists and data analysis experts far outstrips the supply of highly qualified graduates. This is amplified by the fact that there are now very few areas where data analysis skills are not needed.

You’ll be able to pursue a range of careers across sectors, including roles as a:

• Business Intelligence Analyst
• Data Architect
• Data Mining Engineer
• Data Scientist.

Average salaries

According to Prospects.ac.uk, entry-level annual salaries for Data Scientists range from £25,000 to £30,000, rising to £40,000 depending on your experience.

• With a few years’ experience, you could expect to earn £40,000 to £60,000.
• Lead and chief data scientists can earn upwards of £60,000, in some cases reaching more than £100,000.

Lifelong career support

Our career support doesn’t stop when you graduate.

You have access to our free careers service while you study, and beyond.

• 1:1 appointments
• Interview prep
• Job opportunities

Fees and funding

The fee quoted above is based on you starting your studies with us in the 2024/25 academic year.

We have confirmed that our fees will rise by 5% for the 2025/26 academic year. Our indicative cost includes a 5% fee rise each year.

Pay as you go

This is a pay-as-you-go degree.

You do not have to pay the full tuition fee upfront.

You can spread the cost and pay as you go, term by term.

How it works

• You decide how many credits to study for each term.
• At the start of term, you pay only for the credits you’re taking that term.
• This gives you control over your costs and workload for each term of your degree.

Example payment schedule

This is an example payment schedule for tuition fees. It’s based on the typical route through the degree.

You can take more or fewer credits per term than is shown in this example.

Term 1) September – December 2024 | Academic year 2024/25

• Take two 15-credit courses @£1,480 each.
• Fee for term = £2,960.
• Payment due in September.

Term 2) January – April 2025

• Payment due in January.

PgCert exit point

With 60 credits earned, you now have the option to exit with a PgCert.

Total fee for this route to your PgCert = £5,920 .

Term 3) May – August 2025

• Summer break. There is no teaching in the first summer of your degree.
• No payment due.

Term 4) September – December 2025 | Academic year 2025/26

• Take two 15-credit courses @£1,555 each.
• Fee for term = £3,110.

Term 5) January – April 2026

Pgdip exit point.

With 120 credits earned, you now have the option to exit with a PgDip.

Total fee for this route to your PgDip = £12,140 .

Term 6) May – August 2026

• Embark on your 60-credit project.
• Fee for project = £3,520.
• Payment due in May.

Term 7) September – December 2026 | Academic year 2026/27

• Complete your project.
• No further payment is due.

MSc complete

On passing your project, you’ll have earned 180 credits and will be awarded an MSc.

Total fee for this route to your MSc = £15,660 .

Additional costs

Learning resources.

All books and resources you need are included in your tuition fee. They will be available online and you do not have to you buy your own copies.

External hard drive

Because this degree involves handling large data sets, you may have to purchase an external hard drive if you do not already have one, for storing data and coursework.

You may wish to set aside a small budget for printing, depending on how you like to work.

Funding your studies

There are several ways you may be able to get help funding your studies:

• Employer sponsorship – we accept full and partial fee payments from sponsors
• Student loans
• Scholarships – search our funding database for scholarships

Find out more about funding options .

Student card

All our students are entitled to a University of Aberdeen student card. This gives you access to a range of student discounts around the city and online.

20% Alumni discount

You’re entitled to 20% off our postgraduate taught degrees and short courses if you have a degree from the University of Aberdeen. View Alumni discount details .

How discounts work

Discounts are applied during your application process. You can only use one discount per application.

Entry requirements

Msc, pgdip and pgcert.

• A 2:2 (lower-second) honours degree (or equivalent) in any subject.

This is our minimum entry requirement. It is given as a guide and does not guarantee entry.

International students

We welcome students from all over the world.

See the minimum entry requirements above. If you do not have qualifications from the UK, check equivalent qualifications from your country .

Visa requirements

You do not need a student visa to study online with us.

English language requirements

Teaching is delivered in English.

If English is not your first language, use our English requirements checklist to see if you need to provide evidence of your English language skills when you apply.

English language tests and scores

If you do need to provide English language test scores, these are the tests and minimum scores we accept for this course or degree.

These are our Postgraduate Standard requirements.

IELTS Academic and IELTS Online (not IELTS Indicator or IELTS General Training)

• 6.5 overall
• 5.5 for listening, reading and speaking
• 6.0 for writing

TOEFL iBT and TOEFL iBT Home Edition

• 17 for listening
• 18 for reading
• 20 for speaking
• 21 for writing
• TOEFL DI code is 0818

Cambridge English: B2 First, C1 Advanced, or C2 Proficiency

• 176 overall
• 162 for listening, reading and speaking
• 169 for writing

LanguageCert International ESOL B2 Communicator (Written and Spoken)

• Overall High Pass
• 33 for listening, reading and speaking
• 38 for writing

PTE Academic (online test not accepted)

• 59 for listening, reading, speaking and writing

For full information about language requirements, see our English Language Requirements page .

How to apply

You apply through our online Applicant Portal . It allows you to upload relevant qualifications and documents.

Applying to the University of Aberdeen is always free.

What you need to apply for this degree

• Degree transcript
• Personal statement
• Degree certificate
• CV / Resume

Start with our step-by-step guide . It explains degree transcripts, what to write in your personal statement and how to use our Applicant Portal.

When to apply

You can apply to start in either September or January.

Apply as early as you can. This is so we have time to review your application and get a decision to you. We also want to ensure you have time to enrol before teaching starts.

September 2024 intake

For our September 2024 intake, the application deadline is 8 September 2024 .

You will need to accept your offer and provide any outstanding documents to meet the conditions of your offer by 15 September 2024.

Teaching starts on 23 September 2024 .

January 2025 intake

Teaching starts on 27 January 2025 .

Application deadlines will be announced in due course.

IT requirements

Studying Data Science involves storing and processing large datasets. You will need:

A computer (PC, laptop or Mac) operating on either

• Windows 10 or later; in this case you would ideally have a secondary Linux system installed.
• macOS 10.15 (Catalina) or later.

Computer specifications

Any desktop or laptop should be the highest spec you can afford, with:

• a minimum of 16GB RAM (ideally 32GB or more); 8GB will suffice for most content, but will not be sufficient for the analysis of some datasets.
• a good graphics card (ideally, but not necessarily, NVIDIA).
• a fast processor (ideally Intel i5, i7 or i9).
• a sizeable hard drive or SSD (ideally 1TB or above).

External hard drive and memory stick

For storing and backing up your data and coursework, we suggest using an external hard drive with at least 1TB of storage space.

Reliable internet access

We recommend:

• a wired connection
• a minimum 6Mbps download speed and 2Mbps upload speed so you can take part fully in live sessions.

We will occasionally download large datasets or connect to databases, requiring fast connection speeds.

Speakers or headphones

• We recommend a headset with built-in microphone and earphones if you’re likely to study in an environment with background noise.
• A webcam is optional, but you may like to use one from some interactive sessions.

We’ll give you access to Office365 applications. This means you can use online versions of Microsoft Word, Excel, and PowerPoint and install these programs on up to five personal devices.

If a course requires any specialist software, we’ll provide you with access to this and a licence that lasts throughout your studies.

See our detailed IT requirements for more information.

• Short courses

Master of Science

180 credits

£15,660

Apply for this programme

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