Professional Doctorate Big Data Science



The Stirling Professional Doctorate in Big Data is the first industrial doctorate of its kind, supported by The Data Lab. It builds on Stirling’s highly successful taught MSc Big Data, and is designed to equip student professionals with a range of cutting-edge, interdisciplinary research and practical skills and tools, that will lead to an academic or industry job in big data science or data analytics. In addition to enhancing students’ employability through work-based learning, the doctorate prepares students to undertake interdisciplinary Big Data research, jointly supervised by world-leading Stirling academics and Big Data industry experts (for which competitive funding from The Data Lab may be available).

  • Commercial & Scientific Big Data Applications
  • Data Analytics, Machine learning & Natural Language Processing
  • NoSQL Databases, Parallel & Cluster Computing
  • Distributed cluster computing & Map-Reduce

Key information

EU Applicants
EU students enrolling for a postgraduate taught degree in the 2017/18 and 2018/19 academic year will be admitted as Scottish/EU fee status students and will be eligible for the same tuition support as Scottish domiciled students.

  • Qualification: PhD
  • Course Director: Professor Amir Hussain
  • Location: Stirling Campus
Download postgraduate prospectus

Prof. Amir Hussain

Computing Science and Mathematics
University of Stirling

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 big data science
  • Give sound training in relevant practical, investigative, analytical and generic skills required for research in the area of big data science
  • Experience of big data challenges and applications in a wide range of areas, such as Business, Healthcare and scientific disciplines
  • Provide the opportunity to plan, undertake and prepare publication quality research

What makes us different?

World-class library and teaching facilities

Studying for a degree means learning in different ways; managing your own time; conducting research; mastering new computer skills. We have the facilities and advice on hand to help you do all this - and do it well.

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Entry requirements

Academic requirements

We expect students from a variety of backgrounds to apply, these will include:

  • those with numerate and computational degrees (Computing, mathematics, Physics, Engineering)
  • as well as those with a 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.

The doctorate will be geared towards enhancing both interdisciplinary research and employability skills in Big Data science. Students with suitable research-oriented Masters degrees in numerate and computational disciplines (Computing, mathematics, Physics, Engineering), will be considered for direct entry to the 2-year of the Doctoral Training Component, on a case by case basis.

Contact the Programme Director for further details.

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: 6.0 with 5.5 minimum in each skill

Cambridge Certificate of Proficiency in English (CPE): Grade C

Cambridge Certificate of Advanced English (CAE): Grade C

Pearson Test of English (Academic): 54 with 51 in each component

IBT TOEFL: 80 with no subtest less than 17

For more information go to English language requirements


If you don’t meet the required score you may be able to register for one of our pre-sessional English courses. To register you must hold a conditional offer for your course and have an IELTS score 0.5 or 1.0 below the required standard. View our range of pre-sessional courses.

If you don’t meet the required score you may be able to register for one of our pre-sessional English courses. To register you must hold a conditional offer for your course and have an IELTS score 0.5 or 1.0 below the required standard. View our range of pre-sessional courses.

Flexible Learning

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

Fees and costs

Fees for all new applicants to postgraduate taught courses are held at the level set upon entry.

Please note there is an additional charge should you choose to attend a graduation ceremony. View more information

Cost of Living

Find out about the cost of living for students at Stirling

Payment options

Find information on paying fees by instalments

Scholarships & funding

SAAS Postgraduate Loans

The Student Awards Agency for Scotland (SAAS) is now offering a generous loan scheme to assist eligible Scottish and EU-domiciled students pursuing a Masters at the University of Stirling during 2018/19. Find out more about SAAS Postgraduate Loans

Competitive Fully funded places may be available (provided by the European Social Fund and the Scottish Funding Council)

Scholarship finder

Structure and teaching

Delivery and assessment

You will undertake a number of taught modules to equip you with the skills required for big 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.

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.


Autumn semester – year 1

Mathematical and Statistical Foundations

  • Formulae and notation, sets, vectors, graphs, matrices, and functions
  • Probability theory, distributions, expectation and variance, entropy
  • Linear algebra, matrices, inversion, eigenvalues/vectors, Hilbert spaces, tensor products
  • Information theory, likelihood estimation, combinatorics, complexity theory
  • Big data theory, volume, variety, velocity, data sources
  • Statistical methods for estimating correlation structure from datasets
  • Multivariate Statistical Analysis such as Principal component analysis, Factor analysis

Data Manipulation and Representation

  • Structured data representation for storage and distribution
  • Python with Numpy and Scipy

Big Data and NoSQL Databases

  • Relational and Non-Relational Database Design
  • NoSQL Concepts, MongoDB, Cassandra, Neo4j installation, use and deployment
  • Replication and sharding, MapReduce on databases

Commercial and Scientific Big Data Applications

  • Invited lectures from industrial and scientific partners
  • Case studies such as Amazon and Google
  • Scientific data from neuroscience, genetics and environmental science
  • Commercial data for marketing, finance, retail and media

Research Methods

  • Undertaking literature review
  • Qualitative and quantitative research methods
  • Online and offline data gathering methods, Ethical Considerations
  • Data analysis – statistical, machine learning, optimisation, data and text mining tools and techniques
  • Writing of a survey/review paper, research proposals and scientific papers


Spring semester – year 1

Data Analytics, Machine Learning and Visualisation

  • Classification, Regression and Clustering, association rules, time series, feature selection
  • Data project management, CRISP-DM
  • Data collection, preprocessing, evaluation and visualisation
  • Neural networks, Deep Learning, Decision trees, and K-means
  • Natural Language Processing, Sentiment and Opinion Mining
  • Parallel data mining with Mahout

Cluster Computing

  • Distributed and Parallel programming
  • Hadoop and MapReduce, Condor

Research Dissertation Project

  • Students will carry out a systematic review of an appropriately challenging and commercially-relevant applied research topic/area for which they will develop a prototype researched system or a solution for a real industry problem. For Professional/industrial Doctorate candidates, the MRes dissertation will form their Doctorate research proposal, based on this preliminary research.
  • Successful MRes students will have the opportunity to pursue a follow-on, flexible (full-time/part-time) Doctorate research, by working on a single large or coherent set of real-world, industry-led projects (competitive funding available from The Data Lab – more information available on request)

Years 2-3

*MRes leading to Professional Doctorate

Modes of study

Full time study is normally completed in 36 months minimum (Choices to be agreed with Programme Director before module selection).

Part time study is normally completed in 72 months minimum (Choices to be agreed with Programme Director before module selection).

Why Stirling?


In REF2014 Stirling was placed 6th in Scotland and 45th in the UK with almost three quarters of research activity rated either world-leading or internationally excellent.

International Students

The University of Stirling welcomes students from around the world. Find out what studying here could be like for you .

Careers and employability

Career opportunities

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

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

e-Skills UK estimate that the number of Big Data jobs in the UK have risen by 41% from 2012 – 2013. By 2020 there will be 56,000 Big Data jobs in the UK alone with Big Data professionals earning on average 31% more than other IT professionals. Source (


98% of our postgraduate leavers are in employment or further study withing six months of graduating - Destination of Leavers from Higher Education survey 2015/16

The Doctorate programme, equivalent to an Engineering Doctorate (Eng.D), is aimed at a clear and distinct market of professionals seeking to enhance their employability opportunities through applied, impact-led research. You will 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 big 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.

Industry connections

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 with facilitate industry involvement and collaboration and provide funding and resources for students. You can find out more about the Data Lab from their web site: 

The Stirling Professional Doctorate in Big Data has been developed in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Big Data 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, that 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.

Subject to approval

Please note that this course is subject to approval. Courses advertised as 'subject to approval' have successfully completed the first stage of the approval process. The full academic detail is still subject to consideration and approval by the University and is in the final stage of the approval process.

We welcome enquiries for these courses. Please get in touch or register your interest using the online enquiry form.

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