Genomics Data Science

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.

Subjects taught

Year 1 (90 Credits)
Optional MA5114: Programming for Biology - 5 Credits - Semester 1
Optional MA5108: Statistical Computing with R - 5 Credits - Semester 1
Optional MA5116: Introductory Probability for Genomics - 5 Credits - Semester 1
Optional BI5107: Introduction to Molecular and Cellular Biology - 5 Credits - Semester 1
Optional CT5141: Optimisation - 5 Credits - Semester 1
Required BI5102: Genomics Techniques 1 - 5 Credits - Semester 1
Required MA5106: Medical Genomics 1 - 5 Credits - Semester 1
Required MA5111: Genomics Data Analysis I - 5 Credits - Semester 1
Optional MA461: Probabilistic Models for Molecular Biology - 5 Credits - Semester 2
Optional ST412: Stochastic Processes - 5 Credits - Semester 2
Optional ST417: Introduction to Bayesian Modelling - 5 Credits - Semester 1
Optional CT5100: Data Visualisation - 5 Credits - Semester 2
Optional MA216: Mathematical Molecular Biology II - 5 Credits - Semester 2
Optional MA324: Introduction to Bioinformatics (Honours) - 5 Credits - Semester 2
Optional CS4423: Networks - 5 Credits - Semester 2
Optional REM508: Graduate Course in Basic and Advanced Immunology - 5 Credits - Semester 2
Optional MA5118: Advanced Chemoinformatics - 5 Credits - Semester 2
Optional CT5113: Web and Network Science - 5 Credits - Semester 2
Required MA5117: Genomics Research Methods - 5 Credits - Semester 2
Required MA5107: Medical Genomics II - 5 Credits - Semester 2
Required MA5112: Genomics Data Analysis II - 5 Credits - Semester 2
Required MA5105: Genomics Project - 30 Credits - Semester 1

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).

Application dates

Closing Date
Please view the offer rounds website at

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




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

Enrolment dates

Next start date September 2024
Closing Date Offers are made on a rolling basis.

Post Course Info

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 ( 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.

More details
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  • Qualifications

    Degree - Masters (Level 9 NFQ)

  • Attendance type

    Full time,Daytime

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