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Genomics Data Science (MSc)
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.
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 NUI Galway Postgraduate Applications System.
Who Teaches this Course
- Pilib Ó Broin, PhD
- Cathal Seoighe, PhD
- Aaron golden, PhD
- Haixuan Yang, PhD
- Derek Morris, PhD
- Andrew Flaus, 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.
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).
1 year, full-time
Next start date
A Level Grades ()
QQI/FET FETAC Entry Routes
Please view the offer rounds website.
Mode of study
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 InformationCurriculum information relates to the current academic year (in most cases).
Course and module offerings and details may be subject to change.
Glossary of Terms
- 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.
- 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.
- 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.
- A module you may choose to study.
- 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.
- 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)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 2
Why Choose This Course?
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 NUI 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
Transferable Skills Employers Value
Related Student Organisations
Fees: Student levy
Fees: Non EU
Student levy €140 - payable by all students and is not covered by SUSI. Further detail here.