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Genomics Data Science (MSc)
Course Overview
Rapid advancements in high-throughput technologies used to sequence DNA have led to an unprecedented increase in the availability and use of genomics data, from fundamental scientific discovery in the life sciences to clinical applications in precision medicine. The analysis of these large, complex datasets requires a new generation of highly trained scientists who possess not only a sound understanding of the underlying biological principles and technologies, but also the necessary quantitative and computational skills. Combining elements of genetics, statistical science, data analytics, machine learning, bioinformatics and computational biology, this exciting new programme will provide graduates with a highly marketable and transferable set of data science skills as well as specialist knowledge of and experience in the application of these skills to the analysis and interpretation of genomics data.
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You may also be interested in one of our other Mathematics, Bioinformatics and Computational Genomics postgraduate programmes.
Applications and Selections
Applications are made online via the University of Galway Postgraduate Applications System.
Who Teaches this Course
- Pilib Ó Broin, PhD
Requirements and 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
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, physics, statistics, computer science, and engineering (biomedical or electronic/computer engineering).
Additional Requirements
Recognition of Prior Learning (RPL)
Duration
1 year, full-time
Next start date
September 2025
A Level Grades ()
Average intake
10
QQI/FET FETAC Entry Routes
Closing Date
Please view the offer rounds website.
NFQ level
Mode of study
ECTS weighting
90
Award
CAO
Course code
MSC-GDS
Course Outline
The course comprises 90 credits; 60 credits are obtained from taught modules that provide both fundamental and advanced training in genomics data science, 30 credits are obtained from an individual research project. During the first semester, students undertake a number of accelerated-format modules covering molecular and cellular biology, probability and statistics for genomics, programming for biology, genomics techniques, medical genomics, and genomics data analysis. Students also take part in a weekly seminar series which introduces them to the latest developments in genomics data science. Early in the semester, students select their research project topic and begin to engage with the associated scientific literature. During the second semester, students take three core modules including further modules in medical genomics and genomics data analysis, as well as a module in genomics research methods. Students also choose three optional modules from a wide selection of topics across the life science, mathematical, and computational disciplines. These options include: applied and advanced immunology, optimisation, data visualisation, Bayesian modelling, bioinformatics, probabilistic models for molecular biology, mathematical molecular biology, and web and network science. During this semester students complete the literature review component of their project. Following semester two exams, students begin the research phase of their MSc where they work full-time on their research project. At the end of this period, each student submits a manuscript based on their research and gives an oral presentation.
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)
OptionalMA5114: Programming for Biology - 5 Credits - Semester 1OptionalMA5108: Statistical Computing with R - 5 Credits - Semester 1
OptionalMA5116: Introductory Probability for Genomics - 5 Credits - Semester 1
OptionalBI5107: Introduction to Molecular and Cellular Biology - 5 Credits - Semester 1
OptionalCT5141: Optimisation - 5 Credits - Semester 1
OptionalST417: Introduction to Bayesian Modelling - 5 Credits - Semester 1
OptionalST2001: Statistics for Data Science 1 - 5 Credits - Semester 1
OptionalST2003: Random Variables - 5 Credits - Semester 1
OptionalHDS5104: Statistics for Health Data Science - 5 Credits - Semester 1
OptionalHDS5105: Statistical Computing for Biomedical Data - 5 Credits - Semester 1
OptionalCS103: Computer Science - 5 Credits - Semester 1
OptionalMA4103: Machine learning and deep learning for genomics - 5 Credits - Semester 1
RequiredBI5102: Genomics Techniques 1 - 5 Credits - Semester 1
RequiredMA5106: Medical Genomics 1 - 5 Credits - Semester 1
RequiredMA5111: Genomics Data Analysis I - 5 Credits - Semester 1
RequiredMA5105: Genomics Project - 30 Credits - Semester 1
OptionalMA461: Probabilistic Models for Molecular Biology - 5 Credits - Semester 2
OptionalST412: Stochastic Processes - 5 Credits - Semester 2
OptionalCT5100: Data Visualisation - 5 Credits - Semester 2
OptionalMA216: Mathematical Molecular Biology II - 5 Credits - Semester 2
OptionalMA324: Introduction to Bioinformatics (Honours) - 5 Credits - Semester 2
OptionalCS4423: Networks - 5 Credits - Semester 2
OptionalREM508: Graduate Course in Basic and Advanced Immunology - 5 Credits - Semester 2
OptionalMA5118: Advanced Chemoinformatics - 5 Credits - Semester 2
OptionalCT5113: Web and Network Science - 5 Credits - Semester 2
OptionalST2002: Statistics for Data Science 2 - 5 Credits - Semester 2
OptionalST2004: Statistical Inference - 5 Credits - Semester 2
OptionalHDS5101: Predictive Modelling and Statistical Learning - 5 Credits - Semester 2
OptionalHDS5103: Statistical Modelling for Health Data Science - 5 Credits - Semester 2
OptionalMA5121: Genomics at Scale - 5 Credits - Semester 2
OptionalMA5122: Pathogen Genomic Epidemiology and Surveillance - 5 Credits - Semester 2
RequiredMA5117: Genomics Research Methods - 5 Credits - Semester 2
RequiredMA5107: Medical Genomics II - 5 Credits - Semester 2
RequiredMA5112: Genomics Data Analysis II - 5 Credits - Semester 2
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
Fees: Tuition
Fees: Student levy
Fees: Non EU
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.
Postgraduate students in receipt of a SUSI grant – please note 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.
Find out More
Cathal Seoighe
T: +353 91 49 2343
E: cathal.seoighe@universityofgalway.ie
Haixuan Yang
T: +353 89 949 2030
E: haixuan.yang@universityofgalway.ie