Overview
The MSc (Bioinformatics and Computational Biology) may be taken full-time over 12 months or part-time over 24 months from the date of first registration for the programme. The MSc programme has four different streams: for Biology, Mathematics, Statistics and Computer Science graduates, respectively [for graduates of cognate disciplines, the assignment to a particular stream will be decided by the Programme Director].
Part-time students take between five and seven of their twelve taught modules in each academic year and undertake the project in the second academic year. The modules to be taken by the part-time students in each of their two academic years are specified by the course director, and are taught together with the full-time students.
Note: Students cannot choose a module already completed (for example, as part of their undergraduate degree), or with largely overlapping content to a module already completed. Evidence for this would be the production of a transcript showing all modules taken in their previous degree programme(s). The Programme Director will then assist with selecting a suitable replacement module.
Postgraduate Diploma in Bioinformatics and Computational Biology
Students who do not reach the average mark of 50% threshold for the 12 taught modules required to progress to the research dissertation will be conferred with a Postgraduate Diploma in Bioinformatics and Computational Biology.
Similarly, students who pass the taught modules and do not wish to complete the research dissertation, may opt to be conferred with a Postgraduate Diploma in Bioinformatics and Computational Biology.
Programme Requirements
For information about modules, module choice, options and credit weightings, please go to Programme Requirements.
Programme Requirements
Module List
| Code |
Title |
Credits |
| |
| AM6016 | Dynamic Machine Learning with Applications | 5 |
| AM6020 | Open Source Infrastructure for Modelling and Big Data | 5 |
| CS6405 | Datamining | 5 |
| CS6501 | Programming for Bioscientists I | 5 |
| CS6502 | Programming for Bioscientists II | 5 |
| MB6300 | Computational Systems Biology | 5 |
| MB6301 | Genomic Data Analysis | 5 |
| MB6302 | Computational Microbiome Analysis | 5 |
| ST3300 | Data Analysis I | 5 |
| ST4400 | Data Analysis II | 5 |
| ST5005 | Introduction to Probability and Statistics | 5 |
| 5 |
| Discrete Mathematics (5) | |
| Introduction to Relational Databases (5) | |
| MB6303 | Dissertation in Bioinformatics and Computational Biology | 30 |
| |
| ST5005 | Introduction to Probability and Statistics | 5 |
| BC6002 | Molecular Biology | 5 |
| BC6003 | Biomolecules | 5 |
| BL6023 | Cells, Biomolecules, Genetics and Evolution | 5 |
| CS6405 | Datamining | 5 |
| CS6502 | Programming for Bioscientists II | 5 |
| MB6300 | Computational Systems Biology | 5 |
| MB6301 | Genomic Data Analysis | 5 |
| MB6302 | Computational Microbiome Analysis | 5 |
| ST3300 | Data Analysis I | 5 |
| ST4400 | Data Analysis II | 5 |
| 5 |
| Discrete Mathematics (5) | |
| Programming for Bioscientists I (5) | |
| MB6303 | Dissertation in Bioinformatics and Computational Biology | 30 |
| |
| ST3300 | Data Analysis I | 5 |
| or ST4400 | Data Analysis II |
| AM6016 | Dynamic Machine Learning with Applications | 5 |
| BC6002 | Molecular Biology | 5 |
| BC6003 | Biomolecules | 5 |
| BL6023 | Cells, Biomolecules, Genetics and Evolution | 5 |
| AM6020 | Open Source Infrastructure for Modelling and Big Data | 5 |
| CS6405 | Datamining | 5 |
| CS6502 | Programming for Bioscientists II | 5 |
| MB6300 | Computational Systems Biology | 5 |
| MB6301 | Genomic Data Analysis | 5 |
| MB6302 | Computational Microbiome Analysis | 5 |
| 5 |
| Introduction to Relational Databases (5) | |
| Programming for Bioscientists I (5) | |
| MB6303 | Dissertation in Bioinformatics and Computational Biology | 30 |
| |
| AM6016 | Dynamic Machine Learning with Applications | 5 |
| BC6002 | Molecular Biology | 5 |
| BC6003 | Biomolecules | 5 |
| BL6023 | Cells, Biomolecules, Genetics and Evolution | 5 |
| AM6020 | Open Source Infrastructure for Modelling and Big Data | 5 |
| CS6405 | Datamining | 5 |
| CS6502 | Programming for Bioscientists II | 5 |
| MB6300 | Computational Systems Biology | 5 |
| MB6301 | Genomic Data Analysis | 5 |
| MB6302 | Computational Microbiome Analysis | 5 |
| MS6005 | Discrete Mathematics | 5 |
| 5 |
| Introduction to Relational Databases (5) | |
| Programming for Bioscientists I (5) | |
| MB6303 | Dissertation in Bioinformatics and Computational Biology | 30 |
Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards Book and for each module in the Book of Modules.
Programme Learning Outcomes
Programme Learning Outcomes for MSc (Bioinformatics and Computational Biology) (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:
1
1-1
Have a solid background in the theory behind bioinformatics methods and tools so that they can critically evaluate and carry out research in bioinformatics;
1-2
2
Use existing bioinformatics methods and tools and rapidly learn to apply new methods and tools;
1-3
3
Organise and analyse large data sets generated by various 'omics' technologies and systems biology approaches;
1-4
4
Understand the role of modelling and simulation of biological systems;
1-5
5
Have a deep knowledge of the aspect of bioinformatics in which they carried out their three-month research project. This experience will prepare them for a future research or analysis career in the bioinformatics field.
