Health Data Science

This programme is designed to train people from a wide range of backgrounds, and we offer a wide range of pathways via elective modules which are complementary to our health data science core courses. These involve statistics, computer science, genomics, mathematics and health economics.



The MSc in Economics and Finance is designed for individuals from diverse academic and professional backgrounds who are eager to explore the dynamic intersection of economics and finance. Whether you are a recent graduate aiming to build specialist knowledge, a professional looking to advance your career, or someone considering a new direction in the financial sector, this programme offers the skills and expertise to help you succeed.



This course welcomes applicants with a background in economics, finance, business or related disciplines, as well as those with strong analytical skills and a passion for understanding how economic and financial systems operate. It is equally suited to those looking to enhance their career prospects in financial institutions, consulting, policymaking, or international organisations, or to develop the foundations for further academic research.



With a curriculum that emphasises real-world applications and includes topics like study design, statistical computing, machine learning, data visualisation, bioinformatics and statistical modelling, this programme is designed for individuals looking to excel in the dynamic and interconnected fields of data science and health research.



What will I study?



The course is to be taken as a full-time degree taken over an eleven-month period (September to July). The year is divided into two teaching semesters (September to December and January to April). The summer period from May until July will be used to complete a health data science research project and report, with students working on this project throughout the year. The taught elements of the programme comprise six core modules (30 ECTS) during the academic year and a choice of a range of elective options (30 ECTS).

Year 1 (90 Credits)

Optional ST311: Applied Statistics I - 5 Credits - Semester 1

Optional ST313: Applied Regression Models - 5 Credits - Semester 1

Optional ST413: Statistical Modelling - 5 Credits - Semester 1

Optional ST417: Introduction to Bayesian Modelling - 5 Credits - Semester 1

Optional MA215: Mathematical Molecular Biology I - 5 Credits - Semester 1

Optional MA284: Discrete Mathematics - 5 Credits - Semester 1

Optional MA313: Linear Algebra I - 5 Credits - Semester 1

Optional MA385: Numerical Analysis I - 5 Credits - Semester 1

Optional CS4102: Geometric Foundations of Data Analysis I - 5 Credits - Semester 1

Optional MD1602: Introduction to the Ethical and Regulatory Frameworks of Clinical Research - 10 Credits - Semester 1

Optional EC5120: Economics of Health and Health Care - 10 Credits - Semester 1

Optional EC584: Economic Evaluation in Health Care - 10 Credits - Semester 1

Optional CT230: Database Systems I - 5 Credits - Semester 1

Optional CT4101: Machine Learning - 5 Credits - Semester 1

Optional CT5165: Principles of Machine Learning - 5 Credits - Semester 1

Optional CT511: Databases - 5 Credits - Semester 1

Required HDS5106: Health Data Science Research Project - 30 Credits - Semester 1

Required HDS5105: Statistical Computing for Biomedical Data - 5 Credits - Semester 1

Required HDS5102: Clinical Research Design - 5 Credits - Semester 1

Required HDS5104: Statistics for Health Data Science - 5 Credits - Semester 1

Optional ST312: Applied Statistics II - 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 MI439: The Meaning of Life: Bioinformatics - 5 Credits - Semester 2

Optional MA203: Linear Algebra - 5 Credits - Semester 2

Optional MA283: Linear Algebra - 5 Credits - Semester 2

Optional MA378: Numerical Analysis II - 5 Credits - Semester 2

Optional CS4103: Geometric Foundations of Data Analysis II - 5 Credits - Semester 2

Optional CS4423: Networks - 5 Credits - Semester 2

Optional MD515: Systematic Review Methods - 10 Credits - Semester 2

Optional CT5100: Data Visualisation - 5 Credits - Semester 2

Optional ST4140: Modern Statistical Methods - 5 Credits - Semester 2

Optional HDS5108: Causal Inference - 5 Credits - Semester 2

Required HDS5107: Advanced Statistical Computing for Biomedical Data - 5 Credits - Semester 2

Required HDS5103: Statistical Modelling for Health Data Science - 5 Credits - Semester 2

Required HDS5101: Predictive Modelling and Statistical Learning - 5 Credits - Semester 2

Candidates must hold at least a Second-Class Honours Level 8 primary degree in a related subject area. Candidates with degrees from a wide range of areas including healthcare, computing, statistical, mathematical, engineering and business are welcome. Candidates should also have completed at least one introductory statistics module at university level to a satisfactory level.



Those who hold a Level 8 primary degree in a statistical, computational or data science area without honours and have relevant practical experience in the subject area will also be considered.

1 year, full-time

Next start date September 2026

Data science skills are in unprecedented demand from many industries, particularly in healthcare. Data collection and data-led decision making is revolutionising service delivery. Graduates of the MSc in Health Data Science will develop the key statistical and computing skills needed to design studies, analyse complex datasets, and interpret and translate research findings to evaluate health care interventions, services, programmes, and policies.



Our recent graduates have gone on to pursue careers as data scientists in industry and academia, with employers including:



Dunbumby

Royal College of Surgeons Ireland

University of Galway



Other graduates have been awarded scholarships to undertake further studies as part of a PhD in data science and statistical research.