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Fees and Funding

Here's the fees and funding information for each year of this course


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'.

What makes this course different

Hands in front of a laptop

Professional skill development

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

Two people in front of a computer screen

Enhanced knowledge

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

Computer screens

Open Source software tools

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


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 includes 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.




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 is  and 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 UEL 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 remaining 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.


  • All the learning outcomes of the programme are assessed through:
  •  Laboratory session portfolios
  • Coursework
  • Research thesis


Docklands Campus

Docklands Campus, Docklands Campus, London, E16 2RD


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.

Yang Li

Dr Li is an expert in Data Analysis, Data Mining, Data Quality and Geocomputation, and the course leader of MSc and Prof Doc Data Science.

See full profile

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.


This programme uniquely qualifies students in a field that is increasingly recognised as being central to most professional areas and research. The research component provides for 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 membership of the Royal Statistical Society.

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.