Genomics Data Science (MSc)

MSc (Genomics Data Science)

MSc Genomics Data Science Video

The MSc Biomedical Genomics degree programme aims to train graduates with backgrounds in the molecular life sciences in genomics relevant to medical applications.

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Course Overview
Key Facts
Course Outline
Why Choose This Course?
Course Fees
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Course Overview

Rapid advancement in high-throughput DNA sequencing methods has led to an unprecedented increase in the availability and use of genomic data, leading to groundbreaking discoveries in important areas ranging from the life sciences to clinical applications in genomic and precision medicine.

The analysis of large and complex datasets, generated using these cutting-edge techniques, requires a new generation of well-trained scientists, who possess not only the necessary quantitative and computational skills but also a sound understanding of the underlying biological principles.

Combining elements of genetics, statistics, machine learning, data analytics, and computational biology, this exciting programme will provide students with a highly marketable and transferable set of big data science skills, as well as specialist knowledge of and practical experience in the application of these skills to genomic data.

You may also be interested in one of our other postgraduate taught degree programmes Mathematics, Bioinformatics and Computational Genomics

Applications and Selections

Applications are made online via the University of Galway Postgraduate Applications System.

Who Teaches this Course

Pilib Ó Broin, PhD

Professor Cathal Seoighe

PhD

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Dr Matthew Dorman

BA mod, PhD

Lecturer Above The Bar
Clinical Bioinformatics
Mathematical & Statistical Sciences
University of Galway
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Dr Haixuan Yang

MSc, PhD

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Dr Andrew Flaus

BSc, PhD.

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Dr Derek Morris

BSc, Ph.D

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Dr Lars Jermiin

PhD

Lecturer Above The Bar
E: Lars.Jermiin@universityofgalway.ie
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Requirements and Assessment

Requirements

Applicants must have achieved a First Class Honours degree or a 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.

Assessment

Students are formally assessed through a variety of both continuous assessment and end-of-semester written examinations. Continuous assessment include written assignments, programming exercises, genomic analyses, individual and group presentations. Assessment of the research project includes a literature review and manuscript, as well as an oral presentation.

Key Facts

Entry Requirements

Additional Requirements

Recognition of Prior Learning (RPL)

Duration

1 year, full-time

Next start date

September 2025

A Level Grades ()

Average intake

QQI/FET FETAC Entry Routes

Closing Date

Please view the offer rounds website.

NFQ level

Mode of study

ECTS weighting

Award

CAO

Course code

MSC-GDS

Course Outline

This is a 12-month, 90-credit course consisting of 60 credits of taught modules and a 30 credit research project. 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.

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

Optional modules (40 credits from the options below):

Introduction to Molecular & Cellular Biology (5 ECTS)
Graduate Course in Basic & Advanced Immunology (5 ECTS)
Medical Genomics I: Genomics of Common & Rare Diseases (5 ECTS)
Medical Genomics II (5 ECTS)
Genomics Data Analysis I (5 ECTS)
Genomics Data Analysis II (5 ECTS)
Genomics Professional Experience (5 ECTS)
Mathematical Molecular Biology II (5 ECTS)
Introduction to Bioinformatics (5 ECTS)
Probabilistic Models for Molecular Biology (5 ECTS)
Statistics for Health Science Data (5 ECTS)
Statistical Computing for Biomedical Data (5 ECTS)
Introduction to Bayesian Modelling (5 ECTS)
Machine Learning & Deep Learning for Genomics (5 ECTS)
Introduction to Programming (5 ECTS)
Networks (5 ECTS)
Data visualization (5 ECTS)
Web and Network Science (5 ECTS)

Curriculum Information

Curriculum information relates to the current academic year (in most cases).
Course and module offerings and details may be subject to change.

Glossary of Terms

Credits: You must earn a defined number of credits (aka ECTS) to complete each year of your course. You do this by taking all of its required modules as well as the correct number of optional modules to obtain that year's total number of credits.
Module: An examinable portion of a subject or course, for which you attend lectures and/or tutorials and carry out assignments. E.g. Algebra and Calculus could be modules within the subject Mathematics. Each module has a unique module code eg. MA140.
Subject: Some courses allow you to choose subjects, where related modules are grouped together. Subjects have their own required number of credits, so you must take all that subject's required modules and may also need to obtain the remainder of the subject's total credits by choosing from its available optional modules.
Optional: A module you may choose to study.
Required: A module that you must study if you choose this course (or subject).
Required Core Subject: A subject you must study because it's integral to that course.
Semester: Most courses have 2 semesters (aka terms) per year, so a three-year course will have six semesters in total. For clarity, this page will refer to the first semester of year 2 as 'Semester 3'.

Year 1 (90 Credits)

Why Choose This Course?

Career Opportunities

Graduates will be well placed to seek employment in a wide range of industries that employ genomics technologies, including biotechnology and pharmaceutical R&D, as well as clinical healthcare. Graduates will also have the option to pursue PhD research, for example in the University of Galway-led SFI Centre for Research Training in Genomics Data Science (genomicsdatascience.ie). Given the highly transferrable and sought after nature of the data science skills learned, graduates may also choose to enter data analyst or data scientist roles in non-genomics domains.

Who’s Suited to This Course

Learning Outcomes

Transferable Skills Employers Value

Work Placement

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€8,750 p.a. (€8,890 including levy) 2025/26

Fees: Tuition

€8,750 p.a. 2025/26

Fees: Student levy

€140 p.a. 2025/26

Fees: Non EU

€28,000 p.a. (€28,140 including levy) 2025/26

For 25/26 entrants, where the course duration is greater than 1 year, there is an inflationary increase approved of 3.4% per annum for continuing years fees.

Students in receipt of a SUSI grant – An F4 grant is where SUSI will pay €4,000 towards your tuition (2025/26). 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.