Who is this course for?

This programme is designed for students and graduates who want to lead the next wave of discovery in genomics and precision medicine. It is ideal for those eager to develop an understanding of the biological and clinical contexts to work with large, complex biological datasets.

Suited to individuals interested in genetics, statistics, machine learning, data analytics and computational biology, the course equips future scientists with marketable, transferable big-data expertise, biological and clinical knowledge and hands-on experience applying these skills to real genomics data. As companies move toward data-driven discovery and personalised medicine, they need professionals who can integrate genomics data into real-world solutions. This degree equips students with the future-focused analytical and biological skills essential for innovation in the next decade of life-science industries.

What will I study

The course comprises 90 credits, with 60 credits obtained from taught modules, and the remaining 30 credits from a research project, which is done on an individual basis under the supervision of an academic.

Students will enrol in five core modules—all with a focus on genomics—and a range of optional modules, encompassing foundational skills relevant to genomics data science. The set of optional modules available to students is designed to deepen and widen acquired knowledge in the molecular life sciences and/or the quantitative or computational sciences.

Taught modules will be completed by the end of Semester 2 and will consist of 20 credits of core modules and 40 credits of optional modules.

From the end of Semester 2, the student will focus on a full-time basis on an individual research project.

Core modules:

• Genomics at Scale
• Genomics Techniques I
• Genomics Research Methods
• Pathogen Genomic Epidemiology & Surveillance
• Genomics Project

Optional Modules may include:

• Introduction to Molecular & Cellular Biology
• Graduate Course in Basic & Advanced Immunology
• Medical Genomics I: Genomics of Common & Rare Diseases
• Medical Genomics II
• Genomics Data Analysis I
• Genomics Data Analysis II
• Genomics Professional Experience
• Mathematical Molecular Biology II
• Introduction to Bioinformatics
• Probabilistic Models for Molecular Biology
• Statistical Computing for Biomedical Data
• Introduction to Bayesian Modelling
• Machine Learning & Deep Learning for Genomics
• Introduction to Programming
• Networks
• Data visualization
• Statistics for Health Science Data
• Web and Network Science

• Master core concepts: acquire the biological understanding and clinical insights needed to be able to apply computational skills to analyse big datasets generated using the latest genomics techniques
• Enhance your analytical skills: Develop your ability to analyse and interpret biological and genomics datasets using the latest AI and machine learning tools and methodologies
• Wide range of optional modules: enables you to choose the key skills you want to focus on developing.
• Research Project: Apply the knowledge and skills developed during taught modules to tackle an innovative research project
• Strengthen problem-solving abilities: Learn to approach large genomics dataset analysis utilising theoretical and practical computational and biological perspectives.
• Develop professional expertise: Hone the skills required to succeed in diverse roles, including project management teamwork and time management.
• Improve communication skills: Learn to effectively present and articulate findings to a range of audiences, from stakeholders to decision-makers.
• Develop highly sought after skills: the skillsets you will develop are not only applicable to the field of genomics but to any profession involving big data.

With a focus on acquiring an understanding of biological and clinical concepts and further developing students’ data analytical skills this programme will produce graduates with a highly marketable combination of computational and analytical skills and the specialist knowledge of how to apply these skills to analyse large biological datasets. Graduates will be prepared for a wide range of exciting careers in the pharmaceutical industry, healthcare settings and research organisations.  

Industry-Relevant Modules like Statistics for Health Science Data and Data Visualization will give graduates the skills employers are looking for in today's data-intensive environment. You’ll learn to analyse, interpret, and apply genomics data using advanced computational tools, giving you a powerful and distinctive scientific edge.

A Genomics Data Science MSc also provides a solid base for further study and research and is a gateway to progress onto a PhD. Graduates have gone on to further research at PhD level and have acquired positions as lecturers and researchers in third level institutions.

Graduates have also found employment in a range of companies as data scientists and health data analysts, data managers, bioinformaticians, clinical data analysts, managers, consultants and computational biologists. Our graduates fill key posts in the state sector, academia and research and development and recent graduates of this course have gone on to work for companies including:

• Data Scientist, Pfizer
• Data Scientist, Abbott
• Bioinformatician, Enfer Labs
• Bioinformatician, Zinto Labs
• Bioinformatician, Icahn, School of Medicine, Mt Sinai, New York
• Bioinformatics Analyst, Plusvital
• Genomics Data Scientist, Genomics England
• Clinical Data Engineer, ICON
• Clinical Bioinformatician, National Virus Reference Laboratory
• QC Manager, Bristol-Myers squibb

Dr Lars Jermiin

Dr Pilib O’Broin

Professor Cathal Seoighe

Dr Haixuan Yang

Dr Gregory Wheeler

Dr Andrew Flaus

The MSc in Genomics Data Science combines innovative teaching methods with practical, hands-on learning to ensure a comprehensive educational 

experience. You will learn through a mix of interactive lectures, seminars and workshops led by expert faculty. Real-world case studies, data-driven projects and coding exercises will enable you to apply theoretical knowledge to practical problems.

Collaborative activities will enhance your teamwork and communication skills, while individual assignments and the final dissertation will help you develop independence and critical thinking.

Throughout the programme, you will have access to cutting-edge resources, including industry-standard software and real-world datasets, to support your learning and professional growth

How Will I Be Assessed?

Students are formally assessed through a variety of both continuous assessment and end-of-semester written examinations. Continuous assessment will include written assignments, programming exercises, genomic analyses, group and individual presentations, and case studies, while assessment of the Research Project includes examination of a thesis (written in the form of a research paper), as well as oral presentations, and participation in a research seminar series. The assessment components and their weights vary from module to module, but generally include:

1. Continuous Assessment—Regular coursework, including assignments, presentations and in-class tests.  Students receive regular feedback on their progress.
2. Examinations— Written exams take place before Christmas and in April/May. 
3. Project Work— Research projects allow students to apply their skills in real-world contexts and the research project thesis and presentation accounts for 33% of the final mark.

Course queries:
lars.jermiin@universityofgalway.ie 

Programme Director(s):

Dr Lars Jermiin
Lecturer in Bioinformatics, 
School of Mathematical & Statistical Sciences
College of Science and Engineering

E: lars.jermiin@universityofgalway.ie

T: +353 91 49 28 96

Graduates of the MSc in Genomics Data Science will be able to:

• Apply statistical and computational methods to analyse complex datasets.
• Use programming languages such as Python or R to develop reproducible workflows.
• Critically evaluate scientific literature and integrate evidence to inform research questions.
• Design, plan, and manage research projects to completion.
• Communicate scientific findings clearly in written reports, presentations, and discussions.
• Collaborate effectively in interdisciplinary teams across biological, clinical, and computational domains.
• Interpret genomic data within relevant biological and clinical contexts.
• Demonstrate strong time-management and independent working skills in research settings.
• Adhere to ethical, legal, and professional standards in handling and analysing biomedical data.

Visualise and present complex datasets using appropriate computational tools.

Minimum Entry Requirements 

Applicants must have achieved a First or strong Second Class Honours degree in a quantitative discipline.

Qualifying degrees include, but are not limited to, mathematics, statistics, physics, computer science, computational biology, and biomedical, electronic, and computer engineering.

Academic entry requirements standardised per country are available here. 

English Language Entry Requirements 

For applicants whose first language is not English, an English language proficiency of IELTS score of 6.5 is required (with no less than 6.5 in Writing and no less than 6.0 in any other band) or equivalent. 

 More information on English language test equivalency are available here.

 Supporting Documents 

You will be required to provide supporting documentation as part of your application. You can check here what supporting documents are required for this course.  

You can apply online to the University of Galway application portal here. 

Please review the entry requirements set out in the section above.

You will be required to upload supporting documentation to your application electronically. See the section above on entry requirements for further information on the supporting documentation required for this course.

 Closing Dates

For this programme, there is no specific closing date for receipt of applications. Applications will be accepted on a rolling basis and course quotes will be reviewed continuously throughout the application cycle.

 Notes

• You will need an active email account to use the website and you'll be guided through the system, step by step, until you complete the online form.

Browse the FAQ's section for further guidance. 

Fees for Academic Year 2026/27

For 26/27 entrants, where the course duration is greater than 1 year, there is an inflationary increase approved of 1.8% per annum for continuing years fees.

Postgraduate students in receipt of a SUSI grant – please note an F4 grant is where SUSI will pay €4,500 towards your tuition (2026/27).  You will be liable for the remainder of the total fee.  A P1 grant is where SUSI will pay tuition up to a maximum of €6,270. SUSI will not cover the student levy of €140.

Note to non-EU students: learn about the 24-month Stayback Visa here. 

Postgraduate Excellence Scholarships

This scholarship is valued at €1,500 for EU students applying for full-time taught master's postgraduate courses. You will be eligible if:

• You have been accepted to a full-time taught master's course at University of Galway,
• You have attained a first class honours (or equivalent) in a Level 8 primary degree.

An application for the scholarship scheme is required (separate to the application for a place on the programme). The application portal for 2026 is now open and available here. Applications will close on the 30th September 2026. Full details available here.

Global Scholarships  
University of Galway offers a range of merit-based scholarships to students from a number of countries outside of the EU. Visit here for schemes currently available.

Application Process

Students applying for full time postgraduate programmes from outside of the European Union (EU), You can apply online to the University of Galway application portal here. 

Our application portal opens on the 1^st October each year for each the following September.  

Further Information

Please visit the postgraduate admissions webpage for further information on closing dates, documentation requirements, application fees and the application process.