Genomics Data Science

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.



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.

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.



Please see Course Web Page above for module details.

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).

Applications must be completed online at: https://nuigalway.elluciancrmrecruit.com/Apply/Account/Login.



An application requires a registration fee of €35. You will be asked to upload proof of identification, academic transcripts, a personal statement, an academic reference and documentation to fulfil the English requirement (where English is not your first language).

90

1 year full-time

MSc Sustainable Energy & Green Technologies (X413) Full Time

EU fee per year - € 8085

nonEU fee per year - € 25600


***Fees are subject to change

Next start date: September 2025

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 NUIG-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.