The past decade has seen enormous advances in molecular and biomedical technology resulting in an ‘omics’ revolution.
Bioinformatics (health data science) covers the application of mathematics, statistics and computing to biological and clinical scenarios. Algorithms and software tools are used to understand and interpret patient-derived ‘Big Data’
WHAT'S INVOLVED?
You will be use data science tools to analyse clinical and ‘omics data in order to find complex patterns relating to patient response to treatments and prognosis. You will discover results that have the potential to translate to the real world, through clinical trials or commercialisation. Using the skills and tools developed in the course you will derive unique solutions to clinical and biological problems. By the end of the degree you will be ready to work within a multidisciplinary team alongside bioinformaticians, biologists and clinicians.
You will be taught by active researchers from the Patrick G Johnston Centre for Cancer Research http://www.qub.ac.uk/research-centres/cancer-research/, the Welcome Wolfson Institute for Experimental Medicine (http://www.qub.ac.uk/research-centres/wwiem/), and the Centre for Public Health (http://www.qub.ac.uk/research-centres/CentreforPublicHealth/).
This is complemented by guest lectures from industrial and clinical collaborators.
Applicants are advised to apply as early as possible and ideally no later than 31st July 2024 for courses which commence in late September. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal. Notifications to this effect will appear on the Direct Application Portal against the programme application page.
Please note a deposit will be required to guarantee a place on the course. Due to high demand, applications may not be considered if the course has reached its maximum class size and will be placed on a waiting list. Please see deposit terms and conditions for more details.
A data science approach, combining statistics and computer science may provide the key to unlocking the cause/development of various diseases, offering the prospect of developing new drugs and therapies to prevent and treat conditions and diseases.
Bioinformatics and Computational Genomics highlights
I am currently working in Fios Genomics in Edinburgh, Scotland. My work is very closely related to the content of the course, particularly the statistical and genomic analysis sections.
Junior Bioinformatician, Fios Genomics (Graduate)
My MSc in Bioinformatics and Computational Genomics provided me with the opportunity to build highly desirable and transferable skills for today's job market.
1. Students may enrol on a full time (one year) basis. There is an introductory module to Cell Biology during the first two weeks. This is followed by three (20 CAT) modules in Semester 1, and four modules (2 x 20 CAT and 2 x 10 CAT) during Semester 2.
The MSc is awarded to students who successfully complete all taught modules (120 CATS) and a dissertation (60 CATS).
A Diploma exit qualification is available to those students who have successfully completed 120 CATS points of taught modules.
A Certificate exit qualification is available to those students who have successfully completed 60 CATS points of taught modules.
Course Details
Bioinformatics and Computational Genomics is an interdisciplinary field at the heart of health data science research, discovery and practice. With its challenging and rewarding content, this Masters degree will provide students, with a background in computational or life sciences, the opportunity to move into an exciting new area of discovery, technology and application using data analysis. We provide a broad learning base and offer training in open-source programming languages commonly used in academia and industry.
You will begin with an introductory short course (two weeks at the beginning of the first semester) in Cell Biology, followed by compulsory modules in:
SCM8051 Analysis of Gene Expression – 20 CATS
This module will provide the practical molecular biological knowledge required to develop the most effective and useful computational tools for analysis of gene expression data.
SCM8095 Genomics and Human Disease – 20 CATS
This module explores rapidly advancing fields that are moving from specialised research areas to mainstream medicine, science and public arenas. The principles of genomic medicine will be discussed alongside bioinformatics approaches for identifying 'causative genes' for human disease.
SCM7047 Scientific Programming and Statistical Computing – 20 CATS
This module covers the fundamental elements of the statistical framework R and the programming language Python. It gives an introduction to parallel processing applications and implementation and how to leverage modern big-data problems through HPC computing.
SCM8148 Health and Biomedical Informatics and the Exposome (half module 10 CATS)
The module will cover different aspects of health informatics including the basic structure of electronic health records (EHRs). This module also includes an introduction to the concept of the exposome and the contribution of biomedical informatics in exposome research.
SCM8152 Systems Medicine: From Molecules to Populations (half module 10 CATS)
Students will develop knowledge of integrative approaches for multi-'omics biomedical data analysis in order to illuminate disease mechanisms, with applications in precision medicine. Systems medicine brings together multiple scientific disciplines; some of the key areas covered in this module are network biology, machine learning and patient stratification.
SCM8108 Applied Genomics – 20 CATS
This module examines the practical challenges in generating different 'omics' datasets, the important implications of how this is conducted when analysing such datasets and gives practical experience of dealing with resulting datasets using relevant tools.
SCM8109 Biostatistical Informatics (online) – 20 CATS
The core of this module will highlight the analysis of clinico-pathological and 'omics' data. The module will also provide an introduction to carrying out key statistical tests in the R statistical programming language.
Research Project: Dissertation – 60 CATS
Translational bioinformatics and technical development research projects are mainly split between the Patrick G Johnston Centre for Cancer Research and the Wellcome Wolfson Institute for Experimental Medicine and the Centre for Public Health
You will be taught by subject experts from the Patrick G Johnston Centre for Cancer Research (https://www.qub.ac.uk/research-centres/cancer-research/), the Wellcome Wolfson Institute for Experimental Medicine (https://www.qub.ac.uk/research-centres/wwiem/), and the Centre for Public Health (https://www.qub.ac.uk/research-centres/CentreforPublicHealth/). This is complemented by guest lectures from industrial and clinical collaborators.
Environment
You’ll be taught by active researchers including biologists, clinicians and bioinformaticians. We also have teaching input from our industrial partners.
During the research projects, you may have the opportunity to work alongside PhD students in open-plan environments on-campus, but the course is flexible. A suite of high-specification PCs is available for use by students on this course.
We provide a range of learning experiences which enable our students to engage with subject experts, develop attributes and perspectives that will equip them for life and work in an advanced society making use of innovative technologies.
Across a combination of morning and afternoon classes, examples of the opportunities provided for learning on this course are lectures, practical experiences learning technologies and self-directed study to enhance employability.
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Assessment
Assessments associated with the course are outlined below:
Assessment for the modules will be based on 100% coursework/in-class tests/dissertation.
Students who pass all of the taught modules but who fail to achieve a mark of at least 50 per cent in the dissertation are eligible for the award of a PG Diploma.
Students who pass 60 CATS of modules are eligible for the award of PG Certificate
What our academics say
This conversion course provides a solid foundation in data analytical skills which will enable graduates to make the transition into careers in bioinformatics. The modules are delivered by research-intensive academics with industrial and clinical collaborators, ensuring that the course content is kept up to date on cutting-edge techniques.
Jaine Blayney, Lecturer in Translational Bioinformatics, Course Director
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
This module explores rapidly advancing fields that are moving from specialised research areas to mainstream medicine, science, and public arenas. The principles of genomic medicine will be discussed alongside bioinformatics approaches for identifying 'causative genes' for human disease.
Taught sessions will include an introduction to DNA and summarise current knowledge of genomic susceptibility to disease, methodologies, genomic influences for individual responses to drugs, and introduce the impact of epigenetics in human disease.
Learning Outcomes
On completion of this module successful students will be able to:
Describe the structure of DNA and justify options for the investigation of genomic features.
Summarise strategies to ascertain mechanisms of inheritance and describe how genomic medicine contributes to medicine and science.
Discuss how genetic changes may impact on drugs in terms of therapeutic and toxic effects.
Effectively collate and analyse genetic data from a range of formats.
Utilise appropriate tools to analyse data from state-of-the-art genetic techniques.
Critically evaluate genetic tests, including the implications of customised genetic profiling and describe barriers to implementation as part of routine clinical care.
Discuss ethical issues in relation to genomic research and therapeutic implementation.
Explain the flexibility provided by epigenetic mechanisms, challenges for analysis, and describe how this integrates with genetic profiling.
Critically evaluate current scientific literature for genomics and human disease.
Skills
On completion of this module successful students will be able to:
Demonstrate proficiency in advanced computational tools for genomic analysis.
Demonstrate proficiency in written and oral communication skills.
Interrogate relevant online resources for efficient data retrieval and analysis.
Critically evaluate data and scientific literature.
Manage time effectively.
Work individually and as an effective member of a team.
This module covers applications in multi-'omics biomedical data analysis in order to illuminate disease mechanisms, towards the development of new clinical tools. Students will gain knowledge across multiple areas including data integration, machine learning, complexity science and precision medicine.
The module will consist of the following major topics to be covered in lectures and tutorials.
Foundations of systems medicine
Introduction to machine learning
Data processing and integration for systems medicine applications
Reconstruction and analysis of biomedical networks: techniques and principles
Modelling with multiple different datatypes and across scales of biological organisation
Patient stratification in systems and precision medicine
Learning Outcomes
On completion of this module successful students will be able to:
Describe the theoretical foundations of systems medicine
Distinguish properties of key ‘omics datatypes for analysis of the genome, epigenome, transcriptome, proteome and metabolome. Including knowledge of relevant technologies.
Describe and evaluate statistical and machine learning techniques, including applications in integration of ‘omics and clinical data.
Apply survival analysis in the context of systems and precision medicine.
Describe fundamental concepts in graph theory and apply these in the context of network biology.
Perform inference, analysis and visualisation of biomedical networks.
Have awareness of and basic familiarity with key databases and analytical tools.
Describe and evaluate multi-scale analysis approaches, from molecules to populations.
Skills
On successful completion of this module students will have gained or increased skills in:
Critical, analytical and creative thinking
Self-directed learning
Data analysis
Problem solving
Knowledge representation
Organisation, self-motivation, teamwork and time management.
Communication and dissemination of knowledge, including scientific writing.
The Dissertation will be presented as a document of about 15,000 to 20,000 words that contains the following elements:
An abstract of about 250 words summarising the objectives and main research results.
A clearly defined research hypothesis.
A review of related literature.
Brief description of main techniques utilised in the dissertation.
Well documented and illustrated research results that are backed-up by up-to-date evaluation methods.
Discussion of implications derived from research results and potential future directions.
Bibliography.
Students discuss with potential supervisors, submit their project choices and receive their project allocation in Autumn. They complete a literature review by the end of January and the final dissertation is submitted in September.
Learning Outcomes
On completion of this module successful students will be able to:
Summarise the contents of research literature.
Assess published research in a specialised field.
Writing scientific documentations.
Utilise a variety of research techniques.
Properly evaluate research data.
Explain research findings to a wider audience.
Devise research strategies.
"More specifically, students should be able to:
Demonstrate an understanding of the resources required to undertake a project (e.g. material, financial, time, personal)
Formulate clear action plans to deal with the work in an efficient manner including, and where appropriate, the preparation of an application for ethical approval
Demonstrate safe working practices in the laboratory and be aware of their responsibilities with regard to health and safety legislation
Demonstrate effective time-management skills, including punctuality in the meeting of deadlines, e.g. supervisory meetings, literature review, final write-up etc
Demonstrate an appreciation of the requirements for obtaining accurate and valid scientific information
Demonstrate an appreciation of the limits and significance of scientific findings
Demonstrate initiative and independence
Demonstrate a positive commitment in seeing the project through to completion
Acquire project-related technical skills in a competent manner if such skills are a part of the requirement of the project
Demonstrate effective IT skills, including word-processing, retrieval of information from electronic databases and where appropriate, data analysis/statistics
Produce a final project dissertation which should:
Be presented in a clear, coherent and accurate manner throughout.
Describe and discuss previous and contemporary research relevant to the research work presented.
Explain the methodology in sufficient detail to allow reproduction of the work by an independent scientist.
Elaborate a substantive body of research work that has been undertaken by the student.
Formulate a well justified scientific narrative that identifies the underpinning reasoning and the logical progression at each stage of the student's research work.
Describe rigorous analyses of the data obtained, including visualisation and well-reasoned interpretation to identify appropriately qualified findings.
Draw appropriate conclusions from the results obtained in the context of the wider literature, including critical evaluation of the significance and potential importance of these conclusions.
Discuss the limitations of the work and possible future research directions that build upon the work done.
Skills
On completion of this module successful students will have gained or increased competence in:
Critical, analytical and creative thinking.
Analysis and synthesis of concepts derived from published material.
Problem solving.
Data management.
Time management.
Handling various types of IT resources.
Written communication.
This module will provide students with the understanding of the practical challenges in generating different “Omics” datasets, the important implications of how this is conducted when analyzing such datasets and with practical experience of dealing with resulting datasets using relevant tools and the challenges presented
• Introduction to diversity of Omics platforms, applications and datatypes
• Next Generation Sequencing in the laboratory
• Approaches and challenges in sequencing and analysing Cancer Genomes
• Epigenetics and analysis methodologies in normal tissues and disease
• Using ChIP-seq and other enrichment based technologies to understand genomic function
• Challenges and application of integrating data from different platforms
• Evolutionary biology applications of Omics
• Metabolomics application and analysis
Learning Outcomes
On completion of this module successful students will be able to:
List the breadth of Omics platforms and the datatypes they generate
Summarise the strengths and weakness of different single and integrative Omics approaches
Outline the laboratory processing steps required sample preparation from a range of Omics platforms
Discuss the importance and implications of sample preparation for downstream data analysis
Describe the different datatypes generated by a range of Omics platforms
Identify most relevant tools to develop a bioinformatic pipeline for the analysis of a range of Omics platforms
Choose the most appropriate tools for the analysis of data from a range of Omics platforms
Critically evaluate results, experimental design and bioinformatics pipelines for a range of omics experiments
Skills
Critically evaluate their own results and those from the scientific literature
Demonstrate proficiency in the application of a range of bioinformatics tools to analyse complex data
Critically evaluation different “Omics” approaches and experimental designs
Written and oral communication skills
Work effectively as part of team
Manage time effectively
Introduction to R: Basic elements of R, saving/reading data, data structures, control structures.
Visualization of Data: Boxplot, scatter plot, histogram, bar plot.
R packages
R functions and architecture, running scripts, statistical implementation and integrating R in other environments.
Introduction to Python programming, classes and objects. input and output, errors and exceptions, file handling.
NumPy, SciPy and scientific applications of a powerful programming language, String processing.
Parallel computing options and modalities. Recognizing embarrassingly parallel algorithms and options in parallel processing.
High Performance Computing introduction, preparing and submitting jobscripts and leveraging big-data.
Learning Outcomes
On completion of this module successful students will be able to:
Utilise the fundamental elements of the statistical framework R.
Utilise the fundamental elements of the programming language Python.
Discuss parallel processing applications and implementation.
Leverage modern big-data problems through HPC computing.
Skills
On completion of this course successful students will have gained or increased competence in:
Subject-specific skills:
Ability to implement an algorithm using a scientific programming language.
Ability to formulate and devise new algorithmic solutions for problems arising from
biomedical research.
Experience and skills in various computing techniques with emphasis on statistical application.
Transferable skills:
Understanding of the general principles and practical procedures of scientific programming and statistical computing.
Utilisation of a variety of existing libraries, tools and algorithms and incorporate them into their own computing solutions.
Health and Biomedical Informatics and the Exposome
Overview
At the end of this module students will have acquired the knowledge and skills to understand some of the most important concepts in health informatics and how these can be integrated with translational bioinformatics to facilitate the development of precision medicine approaches. This module also includes an introduction to the concept of the exposome and the contribution of biomedical informatics in exposome research.
The module will cover different aspects of health informatics ranging from the basic structure of Electronic Health Records (EHRs) and how the information contained in these resources can be leveraged for translational bioinformatics research.
It will introduce some of the methods and tools (e.g. natural language processing (NLP) or coding tools) relevant for the extraction and reuse of clinical information for research purposes.
The course will, as well, introduce the concept of the exposome, and its relevance for the development of precision medicine approaches. This will include a description of an emerging domain, exposome informatics, providing an insight about the main data types and sources relevant for exposome research.
Finally, the course will provide an integrative perspective and view of how all these elements work together to foster new comprehensive research approaches where biomedical informatics plays a key and central role.
Learning Outcomes
On completion of this module successful students will be able to:
Evaluate the basic components of health informatics;
Evaluate the main coding systems used in health informatics;
Explain the basic concepts associated with the exposome and how it is used in biomedical research;
Evaluate the main online resources containing exposome data and information;
Evaluate and apply different knowledge representation tools;
Integrate and analyse information from disparate data sources;
Combine and analyse data and information from clinical and multi-omics resources.
Skills
On successful completion of this module students will have gained or increased competence in:
Critical, analytical and creative thinking – increasing their knowledge base – identifying resources, gathering information, extracting important information;
Practical skills (analysis skills);
Problem solving abilities – increasing their cognitive abilities – critical thinking, synthesis of information and ideas;
Knowledge representation skills - increasing their ability to understand and use knowledge representation tools
Organisational and personal skills – including responsibility and self-motivation, self-confidence, personal integrity, setting own goals and time management;
Time management skills;
Communication and dissemination – speaking effectively, writing concisely, listening attentively, persuading
Working with others – collaboration, awareness of equality and diversity, leadership skills.
In this module, students will be introduced to data types, data distributions, and hypothesis testing. In addition, they will learn about and be able to evaluate testing for statistical associations and differences between data types including parametric/non-parametric tests. Students will understand what is meant by survival analysis and will be able to carry out both univariate and multivariate analysis. The module will highlight what is meant by experimental study design and student will be introduced to terminology such as ‘study power’. Students will learn how to calculate the power of various different types of studies. The module will outline how to discover patterns in data using unsupervised methods and will also provide an introduction to carrying out statistical tests in the R programming language.
Learning Outcomes
On completion of this module successful students will be able to:
Explain the basic principles of statistical methods used in biomedical/medical sciences.
Apply multiple hypothesis testing corrections.
Utilise the programming language R to apply statistical methods.
Explain and apply survival analysis.
Utilise unsupervised learning methods.
Apply methods for dimension reduction.
Process and analyse common ‘big data’ platforms.
Skills
On completion of this course successful students will have gained or increased competence in:
Subject-specific skills:
Ability to interpret results from a statistical analysis in biomedical terms.
Ability to apply a wide range of techniques to new problems and data types arising from biomedical research.
The students will learn how to utilise statistical programming skills.
Transferable skills:
The students will gain an understanding of statistical methods and statistical thinking.
The students will learn how to select appropriate analytical methods for biomedical problems.
The students will develop analytic thinking.
The students will learn to communicate results of analyses to a multidisciplinary audience.
The module will contribute to a deeper appreciation and understanding of statistical programming skills.
This module will provide the practical molecular biological knowledge required for students to develop the most effective and useful computational tools for analysis of gene expression data.
• Gene expression
o Regulation of gene expression in vivo
• Experimental design
o Hypotheses
o Model experimental systems
Cell culture, small animal
o Quality control, RNA
• Tools for measuring gene expression
o ‘Conventional’, qRT-PCR, immunocytochemistry
o High throughput
Microarray
‘Massively parallel’ or ‘deep‘ sequencing: RNA-Seq, small RNAs, chromatin immunoprecipitation and methylation,
CHIP-Seq, CLIP-Seq
Proteomics
Metabolomics
o Single cell RNA sequencing
o Spatial transcriptomics
o Strengths and limitations of different data types
• Data analysis
o Normalisation
o Clustering
o Gene ontology
o Pathway analysis
o Metagenomics
o Practical RNA-Seq data analysis with Python
• Data and repositories
Practical examples of gene expression analyses from cancer and other fields of research, including epigenetics and functional metagenomics
Learning Outcomes
On completion of this module successful students will be able to:
List the ways in which gene expression is regulated in vivo.
Summarize the limitations of different types of gene expression data.
Evaluate the strengths and limitations of specific model systems.
Outline the practical steps involved in performing a microarray, next generation RNA sequencing or proteomic profiling analysis.
Discuss the applications and practical challenges of studying single cell gene expression.
Give practical examples of how analyses of gene expression have advanced scientific knowledge.
Justify the need for a thorough knowledge of molecular biology to become an effective Bioinformatician.
Design a gene expression experiment to address a given biological question, including appropriate samples, a data analysis pipeline and independent validation.
Choose the most appropriate data analysis strategy to maximise extraction of biologically relevant information.
Skills
On completion of this course successful students will have gained or increased competence in:
Subject-specific skills:
Critically evaluate scientific literature involving molecular and bioinformatic analyses.
Apply a range of bioinformatics tools to analyse complex data.
Demonstrate originality and creativity in the development and application of advanced knowledge.
Transferable skills:
Critically assess and evaluate their own and other’s work.
Demonstrate proficiency in written and oral communication skills.
Apply interpersonal skills to work effectively in a team
Time Management
A 2.1 Honours degree or equivalent qualification acceptable to the University in a Natural Science subject, Mathematics, Computer Science, or a relevant medical or life sciences subject (e.g. Genetics, Molecular Biology, Biomedical Sciences, Physics or Statistics). A medical (MB) or dental degree (BDS) is also considered.
Applicants must have completed (and passed at equivalent of UK 2:1 standard) a subject/module from any of the following groups:
1. genetics/genomics/molecular biology/biomedical science
2. chemistry/medicinal chemistry/biochemistry
3. mathematics/statistics-related subjects
4. computing/computer science/informatics.
Intercalating Medical and Dental Applicants:
i) QUB:
Intercalating medical and dental students within QUB will be considered if:
a) they have successfully completed the third year of their course at first attempt and
b) have achieved at least an Upper Second Class Honours degree standard.
c) have permission to intercalate from either the Director of Medical Education or Dentistry as appropriate.
ii) External:
An external medical or dental student wishing to intercalate will be considered if:
a) They have successfully completed all assessments at first attempt for the year in which they are applying.
b) Achieved at least an overall Upper Second Class Honours degree standard as determined by their University
iii) International:
• Applicants who are currently studying an overseas Medical (e.g. MBBS or MBChB) or Dental degree at a recognised institution acceptable to the University, may apply.
• Applicants must have passed all assessments at first attempt for the year in which they are applying, normally 3rd year for those completing a 5 year programme or 4th year for those completing a 6 year programme.
• Applicants may be required to provide details of the medical or dental curriculum they are studying in order to confirm compatibility.
Applicants are advised to apply as early as possible and ideally no later than 31st July 2024 for courses which commence in late September. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal.
Please note: A deposit will be required to secure a place on this course.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
An IELTS Academic test score of 6.5 overall with a minimum of 6.0 in each of the four elements or an equivalent qualification acceptable to the University (taken within the last 2 years). IELTS test result/qualification must be submitted by 30 June 2024.
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
The rapid production of 'omics' data within medicine and the life sciences has meant that individuals with health data science experience in this field are highly sought after. Recent graduates have gone on to work in industry in companies such as Almac Diagnostics, Liberty IT and Fios Genomics and some have gone onto further PHD level research. http://www.qub.ac.uk/directorates/sgc/careers/
Employment after the Course
Many of our students go on to pursue further PhD study in health data science at Queen’s and further afield. Others go on to work in a variety of roles in both the private and public sector here in Northern Ireland and internationally, including the following:
Bioinformatician at Belfast Health and Social Care Trust
Application Scientist at Dotmatics
Network and Security Engineer at Darktrace
Senior Data Scientist at Liberty IT
Graduate Trainee HPC, University of Bristol
Junior Bioinformatic Scientist at Almac Group
Bioinformatician at Fios Genomics Ltd
Biomedical Scientist and Junior Bioinformatician, BioKinetic Europe
Data Analyst. Diaceutics http://www.qub.ac.uk/directorates/sgc/careers/
Professional Opportunities
Queen's postgraduates reap exceptional benefits. Unique initiatives, such as Degree Plus and Researcher Plus bolster our commitment to employability, while innovative leadership and executive programmes alongside sterling integration with business experts helps our students gain key leadership positions both nationally and internationally. http://www.qub.ac.uk/directorates/sgc/careers/
Graduate Plus/Future Ready Award for extra-curricular skills
In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.
1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.
2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
Students have the option to hire a locker, at a cost of £5 per student per year. Students will need access to their own computing facilities as part of this programme is delivered online.
Terms and Conditions for Postgraduate applications:
1.1 Due to high demand, there is a deadline for applications.
1.2 You will be required to pay a deposit to secure your place on the course.
1.3 This condition of offer is in addition to any academic or English language requirements.
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.
Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.
Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.
If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.
Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.
There are also additional charges for graduation ceremonies, examination resits and library fines.
How do I fund my study?
The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.
A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.