Introduction

Health Data Science combines health research, statistics and computing sciences to address health and care problems using data. Our MSc programme enables students from both healthcare and computational backgrounds to develop their health data science skills, supported by an interdisciplinary team of academics, NHS and industrial partners.

The School of Medicine, Medical Sciences and Nutrition, in association with The Data Lab, is offering up to four scholarships to cover tuition fees for Scotland-based students joining the MSc Health Data Science programme in September 2021. Find out more here.

Study Information

At a Glance

Learning Mode
Blended Learning
Degree Qualification
MSc
Duration
12 months or 24 months
Study Mode
Full Time or Part Time
Start Month
September
Location of Study
Aberdeen

Nationally and internationally there is a critical shortage in data intensive analytic capacity applied to healthcare. The effective and efficient use of data has the potential to create the transformative step change needed through targeting health and care improvement.

Our interdisciplinary MSc in Health Data Science aims to develop the next generation of health data scientists. The programme is delivered by academics from our Aberdeen Centre for Health Data Science (ACHDS). The Centre for Health Data Science aims are to create innovative, interdisciplinary, data science solutions to the big challenges for health and health care, to improve health for individuals, local communities and globally.

The programme offers a wide range of specialist elective options to allow students to build a personalised masters depending on their background, interests and skills and includes a work-based placement or research project. At the end of the programme students will be able to evaluate, critique and demonstrate competency in the practical application of current data intensive analytical methods and current health and care research methods.

The MSc is ideally suited to healthcare professionals who wish to develop data health science skills or for students from computational and/or data intensive science backgrounds who wish to work in the health sector.

What You'll Study

Semester 1

Semester 1

Compulsory Courses

Students must complete the following compulsory courses.

  • Applied Statistics (PU5017)
  • Open and Reproducible Health Data Science (PU5550)
Introduction to Data Science (PX5008)

15 Credit Points

In this course we study the typical workflow for a data analysis project. We will learn how to access and collect data, how then to clean the data, and organise it in databases to prepare it for later analysis.

We will then perform descriptive and exploratory data analysis and finally visualise the results and create a report.

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Optional Courses

Students must also complete one optional course.

Foundations of AI (CS5060)

15 Credit Points

This course presents the fundamental techniques of Artificial Intelligence, used in system such as Google Maps, Siri, IBM Watson, as well as industrial automation systems, and which are core to emerging products such as self-driving vehicles. This course will equip the student to understand how such AI technologies operate, their implementation details, and how to use them effectively. This course therefore provides the building blocks necessary for understanding and using AI techniques and methodologies.

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Database Systems and Big Data (CS5097)

15 Credit Points

This course will be of interest to anyone who wishes to learn to design and query databases. The course aims to teach the material using case studies from real-world applications. You will develop a critical understanding of the principal theories, principles and concepts, such as modelling techniques used in the design, administration and security of database systems. You will also learn core theoretical concepts such as relational algebra, file organisation and indexing. At the end of this course you will be able to design and build Web and cloud-based databases and have a critical understanding of how database-driven applications operate.

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Introduction to Programming (PX5007)

15 Credit Points

This course teaches programming in high level languages and in particular the Wolfram Language (Mathematica). It will introduce all areas of this powerful language, including symbolic and numerical calculations and simulations, links to other high level languages such as R and Python, links to database languages mySQL and Mongo.

We will show how Wolfram Language allows computation to be applied to many areas of data analysis, and modelling. This allows us to gain deep insight into systems.

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Machine Learning (PX5009)

15 Credit Points

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

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Fundamentals of Research Design (PU5027)

15 Credit Points

Fundamentals of research design provides the student with skills in both quantitative and qualitative design enabling the student to plan ethical research in a health context. Students are taken through each step - from formulating the research question, to study design, sample selection, methods for data collection to dissemination of results.

Upon completion of this course you will be able to:

  1. Summarise the key factors involved in the research process
  2. Evaluate the appropriateness of study designs commonly used in applied health research studies
  3. Identify the advantages and disadvantages associated with quantitative and qualitative research methodologies
  4. Develop a research proposal to answer a health-related research question which maximises research quality and rigour.v
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Health Economics (PU5032)

15 Credit Points

The aim of this course is to provide an introduction to the application of economics to health care. Resources available for the provision and payment for health care are limited. However, knowledge of economics helps ensure that available resources are used in the most effective way possible. Economics allows more informed decision making about a variety of issues: choosing between alternative treatments; setting priorities between patients; choosing between alternative new technologies; organising the provision of health care.

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Designing Real - World Trials (PU5038)

15 Credit Points

Randomised Controlled Trials (RCTs) are used to test the effectiveness of interventions. The aim of this course is to take a student through the process of designing RCTs. The course will focus on RCTs in the evaluation of real world healthcare and public health settings.

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Evidence - Based Health (PU5031)

15 Credit Points

This course aims to enable the fundamental understanding and application of evidence based health at an individual- and population-level, focusing on the use of systematic reviews to synthesise evidence as well as methods to translate and implement evidence to inform health practice and policy. Students on this course will work through the stages of a systematic review unit by unit, completing interactive and practical exercises to develop the skills required to conduct a review. They will then study how the evidence produced from systematic reviews is used to shape health policy and practice, at the level of organisations and individual clinical encounters.

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Semester 2

Semester 2

Compulsory Courses

Students must complete the following compulsory courses.

  • Epidemiology (PU5538)
Health Informatics (PU5532)

15 Credit Points

We live in a time of ‘Big Data’ with the rapid growth in the digital capture of health information. Health Informatics is the science of data capture, linkage and analysis of large datasets to improve health. The demand for health researchers with training and experience in health informatics is high. For people practicing in Public Health, it is a key skill. It will equip students for any career in health research or public health practice and this course is an excellent stepping stone for those wishing to develop a specialist interest in the field.

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Optional Courses

Students must also complete two optional courses.

Data Science: from Data to Insight (CS5703)

15 Credit Points

Data Science is an interdisciplinary field that seeks to identify and understand phenomena captured in structured or unstructured data, extract insights, and add value by generating predictions that aid optimization of processes and equipment. These techniques show considerable promise for bringing about a revolution, increasing the significance and value of owning and collecting data of all types. This course introduces the common techniques and considers the implications for data managers.

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Data Science: from Data to Insight (CS551L)

15 Credit Points

Data Science is an interdisciplinary field that seeks to identify and understand phenomena captured in structured or unstructured data, extract insights, and add value by generating predictions that aid optimization of processes and equipment. These techniques show considerable promise for bringing about a revolution, increasing the significance and value of owning and collecting data of all types. This course introduces the common techniques and considers the implications for data managers.

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Values and Ethics in Public Health (PU5528)

15 Credit Points

This course challenges you to engage robustly with questions about what is good and right (and why) in public health policy and practice. You will develop your ability to critique and participate effectively in debates about what matters – and what is morally justified - in efforts to improve the health and wellbeing of communities and populations. You will develop the knowledge and confidence to identify value-based assumptions as you examine a range of real-world health problems and practice justifying and objecting to different strategies for addressing them

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Systematic Reviewing (PU5526)

15 Credit Points

This course will equip students with the relevant skills to interpret and conduct systematic reviews on the effectiveness of healthcare interventions. Using lectures and practical sessions, students will understand the principles of systematic reviewing and the differences between narrative and systematic reviews. They will learn to formulate a clear research question and undertake each stage of systematic reviewing of randomised controlled trials. They will also learn about the importance of the levels of evidence and systematic reviews of other different study designs. This course will also introduce the students to advances in systematic reviews such as network meta-analysis and use of Individual Patient Data (IPD)

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Health Economics (PU5546)

15 Credit Points

Resources available for the provision and payment for health care are limited. However, knowledge of economics helps ensure that available resources are used in the most effective way possible. Economics allows more informed decision making about a variety of issues: choosing between alternative treatments; setting priorities between patients; choosing between alternative new technologies; organising the provision of health care.

In this course students will acquire a knowledge and understanding of:

  • Key themes of economic theory applied to health care
  • Some of the main techniques of health care evaluation
  • Main arguments concerning alternative systems for organising and financing health care
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Work - Based Placement in Applied Health Sciences (PU5548)

15 Credit Points

This work-based placement elective offers a professional placement with a government/public, civic or voluntary health and/or development sector organisation. You will undertake a ten-week placement with your host organisation, either within the organisation, remotely from Aberdeen, or using a combination of both. Placements are subject to availability and are offered on a competitive basis.

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Machine Learning (PX5509)

15 Credit Points

In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.

Great emphasis will be given to the actual application of all these methods to small and large data sets.

View detailed information about this course
Semester 3

Semester 3

Compulsory Courses

Students have the choice of completing one 60 credit Masters Research project or two 30 credit courses from a choice of PU5916, PU5919 or PU5923.

Masters Research Project (PU5922)

60 Credit Points

This course offers students the opportunity to complete a substantial piece of data-driven, empirical work within their field of study under the supervision of an experienced researcher.

Topics available will be varied but within the domain of their field of study. Alongside supervisors, students will identify a suitable topic area, describe an appropriate study design and implement an empirical study to be completed within a laboratory setting. Students will be involved alongside the supervisors in the process of defining the research question, and developing the research plan and, where appropriate, obtaining regulatory approvals. This course is for non-laboratory based projects (if you are intending to undertake a project in a scientific laboratory setting you should register on MB5913)

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Rapid Research Project (PU5916)

30 Credit Points

This course offers students the opportunity to gain experience of the different processes involved in scientific research or other scholarly investigation in either in a laboratory, clinical, academic or public health setting, under the supervision of an experienced researcher. Topics available will be varied but within the domain of their field of study. All projects offered will have a defined research question, a full protocol and where appropriate all regulatory approvals before the start of the project.

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Evaluating Policy Effects in Practice (PU5919)

30 Credit Points

The case study provides students with the opportunity to select, design, conduct and report on a detailed systematic investigation of a topic within an applied health field. The aim is to demonstrate an in-depth understanding of a particular topic, including the academic background, relevant policy and the roles played by various actors and agencies. The case study is conducted in an academically robust and ethically sensitive manner.

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Extended Work - Based Placement in Applied Health Sciences (PU5923)

30 Credit Points

This work-based placement elective offers a professional placement with a government/public, industrial, civic or voluntary

health and/or development sector organisation. Students will undertake a ten-week placement with a host organisation, either within the organisation, remotely from Aberdeen, or using a combination of both. University of Aberdeen staff will co-supervise student placements. Placements are subject to availability and are offered on a competitive basis.

View detailed information about this course

We will endeavour to make all course options available; however, these may be subject to timetabling and other constraints. Please see our InfoHub pages for further information.

How You'll Study

The programme is delivered via blended learning and will involve a variety of teaching methods including face to face lectures, tutorials, PowerPoint videos with accompanying self-tests and quizzes, video interviews with accompanying guides, computer-based practical sessions, self-study of recommended reading material and discussion boards. This mixed approach to teaching ensures that a range of different learning opportunities are provided for all students.

Learning Methods

  • E-learning
  • Group Projects
  • Individual Projects
  • Lectures
  • Peer Learning
  • Professional Placements
  • Research
  • Seminars
  • Tutorials
  • Workshops

Assessment Methods

A variety of different approaches are combined to assess student understanding, progress and performance throughout the programme. Both formative and summative assessments include reflective learning, writing computer programmes, written and activity-based assessments, structured and multiple-choice questions. Whenever possible, the assessment will utilise real life scenarios and/or data and equip the student with transferable skills for subsequent careers.

Why Study Health Data Science?

  • The programme is delivered by staff from our Institute of Applied Health Sciences, a large multidisciplinary grouping of University staff and the Aberdeen Centre for Health Data Science (ACHDS). The ACHDS has an established global reputation in the health and data sciences harnessing academia, NHS, industry and public partnerships.
  • Courses will be taught by academics and international experts from the Schools of Medicine, Medical Sciences and Nutrition, Natural and Computing Science and Business, the NHS and industry giving students the opportunity to learn from an interdisciplinary  team.
  • Students will have the opportunity to participate in a student-led workshop series throughout the programme, mentored by members of the ACHDS team. Students will work with our NHS, local authority, industry and innovation partners to apply their skills and knowledge to real world scenarios.
  • At the end of the programme, students will be asked to coordinate and deliver a showcase symposium which will be live streamed. They will present their project work and network with partners and collaborators. Building transferable skills, the students will have opportunities in leadership, communication, event planning and, with the commitment of our partners, a key opportunity to network.
  • Enhancing student’s employability is a key focus of the programme and engagement with potential employers will be facilitated by our existing partnerships with companies and public bodies including the NHS and Opportunity North East (ONE) business development framework.

What Our Students Say

Lorna Henderson

Lorna Henderson

Lorna Henderson

My favourite part of the degree has been learning new skills from lecturers with real-life experience of solving data science challenges. My classmates are friendly and come from a range of backgrounds, giving a rich and diverse learning experience.

Entry Requirements

Qualifications

The information below is provided as a guide only and does not guarantee entry to the University of Aberdeen.

Applicants will usually possess an undergraduate degree at a minimum of 2:1 or equivalent in a relevant discipline including life sciences, medicine, computing and maths. Other relevant work experience will also be considered.

Please enter your country to view country-specific entry requirements.

English Language Requirements

To study for a Postgraduate Taught degree at the University of Aberdeen it is essential that you can speak, understand, read, and write English fluently. The minimum requirements for this degree are as follows:

IELTS Academic:

OVERALL - 6.5 with: Listening - 5.5; Reading - 5.5; Speaking - 5.5; Writing - 6.0

TOEFL iBT:

OVERALL - 90 with: Listening - 17; Reading - 18; Speaking - 20; Writing - 21

PTE Academic:

OVERALL - 62 with: Listening - 59; Reading - 59; Speaking - 59; Writing - 59

Cambridge English B2 First, C1 Advanced, C2 Proficiency:

OVERALL - 176 with: Listening - 162; Reading - 162; Speaking - 162; Writing - 169

Read more about specific English Language requirements here.

Document Requirements

You will be required to supply the following documentation with your application as proof you meet the entry requirements of this degree programme. If you have not yet completed your current programme of study, then you can still apply and you can provide your Degree Certificate at a later date.

CV
an up-to-date CV/Resumé
Degree Certificate
a degree certificate showing your qualifications
Degree Transcript
a full transcript showing all the subjects you studied and the marks you have achieved in your degree(s) (original & official English translation)
Personal Statement
a detailed personal statement explaining your motivation for this particular programme

Fee Information

Fee information
Fee category Cost
EU / International students £21,500
Tuition Fees for 2021/22 Academic Year
Home / RUK £9,200
Tuition Fees for 2021/22 Academic Year

Additional Fee Information

  • In exceptional circumstances there may be additional fees associated with specialist courses, for example field trips. Any additional fees for a course can be found in our Catalogue of Courses.
  • For more information about tuition fees for this programme, including payment plans and our refund policy, please visit our InfoHub Tuition Fees page.

Funding Opportunities

The School of Medicine, Medical Sciences and Nutrition, in association with The Data Lab, is offering up to four scholarships to cover tuition fees for Scotland-based students joining the MSc Health Data Science programme in September 2021. Find out more here.

Scholarships

Eligible self-funded international Masters students will receive the Aberdeen Global Scholarship. Visit our Funding Database to find out more and see our full range of scholarships.

Careers

Health Data Science is a rapid growth area and the Academy for Medical Sciences and Health Foundation have both identified the significant shortage of skilled workforce in this field. This shortage is recognised in both the UK and internationally.

Graduates will have the skills needed for a range of careers in Academia, NHS and Industry that involve the analysis and interpretation of health data. Students completing this programme with a commendation or above would also be eligible for consideration to undertake PhD study in this area.

Industry Links

Enhancing student’s employability is a key focus of the programme and engagement with potential employers will be facilitated by our existing partnerships with companies and public bodies including the NHS and Opportunity North East (ONE) business development framework.

Accreditation

The work-based placement course (PU5548 Work-based Placement in Applied Health Sciences) which is offered as part of this programme has been accredited by the Scottish Innovative Student Award (SISA) scheme.

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Top 20 UK University

The University of Aberdeen is ranked 20th in the UK by the Guardian University Guide 2021

Our Experts

Other Experts
Dr Kathryn Martin
Dr Mintu Nath
Dr Heather Morgan
Professor Nir Oren
Professor Lesley Anderson
Programme Coordinators
Professor Corri Black
Dr Dimitra Blana

Information About Staff Changes

You will be taught by a range of experts including professors, lecturers, teaching fellows and postgraduate tutors. Staff changes will occur from time to time; please see our InfoHub pages for further information.

Facilities

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Health Sciences Building

Health Sciences Building

The Health Sciences building, located on the Foresterhill Health Campus, houses the purpose built Clinical Research Facility, researchers from the Institute of Applied Health Sciences and the Imaging Department which boasts state-of-the-art equipment

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Foresterhill Health Campus

Foresterhill Health Campus

The Foresterhill Health Campus is one of the largest clinical complexes in Europe which includes the Medical School, large teaching hospital, the Institute of Medical Sciences and the Rowett Institute of Nutrition and Health.

Aberdeen Centre for Health Data Science

The Centre for Health Data Science is a partnership between the University of Aberdeen, NHS Grampian and NHS Research & Development North Node.

Find out more

Get in Touch

Contact Details

Address
Student Recruitment & Admissions Service
University of Aberdeen
University Office
Regent Walk
Aberdeen
AB24 3FX