Who is this course for?  

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). 

Core modules: 

• Statistics for Health Data Science
• Clinical Research Design
• Statistical Computing for Biomedical Data 1 and 2 (Semesters 1 and 2)
• Statistical Modelling for Health Data Science
• Predictive Modelling and Statistical Learning 

Optional Modules may include: 

• Introduction to Programming
• Machine Learning
• Data Visualisation
• Modern Statistical Methods
• Introduction to Bioinformatics
• Introduction to Bayesian Modelling
• Economic Evaluation in Health Care
• Applied Statistics 

Curriculum Information

• Master core concepts: Develop a solid foundation in statistics and programming, applying them to real-world contexts in healthcare and health research.
• Enhance analytical skills: Build the ability to process, visualise and interrogate data using advanced tools and methodologies.
• Strengthen problem-solving abilities: Learn to approach statistical and programming challenges critically, using both theoretical and practical perspectives.
• Learning how to think statistically: Learn to assess evidence and claims in health research critically, understanding uncertainty in estimation and potential for bias. 
• Improve communication skills: Learn to effectively present and articulate findings to a range of audiences. 

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. 

• Prof. John Newell
• Prof. Andrew Simpkin
• Dr Neil O’Leary
• Dr Davood Roshan Sangachin
• Prof. Carl Scarrott
• Dr Nicola Fitz-Simon
• Dr John Ferguson
• Dr Paul Wilson

How will I learn? 

The MSc in Health Data Science combines innovative teaching methods with practical, hands-on learning to ensure a comprehensive educational experience. You will learn through a mix of interactive lectures, seminars and workshops led by expert faculty. 

The MSc in Health Data Science is run by our highly successful Sonraí Health Data Science Research Cluster, who are statisticians and bioinformaticians working in health, sports science and related fields within data science. This ensures that the syllabus and material for core modules are informed by the latest developments and best practice in data science and by the important challenges to be met in modern healthcare and health research. 

The 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 data science core courses. These involve statistics, computer science, genomics, mathematics and health economics. Real-world case studies, data-driven projects and coding exercises will enable you to apply theoretical knowledge to practical problems. 

Group projects and collaborative activities will enhance your teamwork and communication skills, while individual assignments, critical appraisals and programming projects will help you develop independence, critical thinking and coding skills. 

The year is divided into two teaching semesters (September to December and January to April). 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). The summer period from May until July will be used to complete a health data science research project and report (30 ECTS), with students working on this project throughout the year. 

How Will I Be Assessed? 

Throughout the programme, your progress is assessed through various coursework, in-class assessments and written exams, including reports, presentations, and computing assignments. 

Course queries: 
msc.hds@universityofgalway.ie 

Programme Director(s):  

Dr Neil O’Leary,  
Lecturer in Statistical Science 
School of Mathematical and Statistical Sciences
College of Science and Engineering 
E: neil.oleary@universityofgalway.ie 

Minimum Entry Requirements  

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. 

Academic entry requirements standardised per country are available here.  

English Language Entry Requirements  

For applicants whose first language is not English, an English language proficiency of IELTS score of 6.5 is required (with no less than 6.5 in Writing and no less than 6.0 in any other band) or equivalent.  

More information on English language test equivalency are available here.  

Supporting Documents  

You will be required to provide supporting documentation as part of your application. You can check here what supporting documents are required for this course.   

You can apply online to the University of Galway application portal here.  

Please review the entry requirements set out in the section above.  

You will be required to upload supporting documentation to your application electronically. See the section above on entry requirements for further information on the supporting documentation required for this course.  

Closing Dates 

For this programme, there is no specific closing date for receipt of applications. Applications will be accepted on a rolling basis and course quotas will be reviewed continuously throughout the application cycle.  

Notes 

• You will need an active email account to use the website and you'll be guided through the system, step by step, until you complete the online form.
• Browse the FAQ's section for further guidance.  

Fees for Academic Year 2025/2026

For 25/26 entrants, where the course duration is greater than 1 year, there is an inflationary increase approved of 3.4% per annum for continuing years fees.

Postgraduate students in receipt of a SUSI grant – please note an F4 grant is where SUSI will pay €4,000 towards your tuition (2025/26).  You will be liable for the remainder of the total fee.  A P1 grant is where SUSI will pay tuition up to a maximum of €6,270. SUSI will not cover the student levy of €140.

Note to non-EU students: learn about the 24-month Stayback Visa here. 

Postgraduate Excellence Scholarships  

This scholarship is valued at €1,500 for EU students applying for full-time taught master's postgraduate courses. You will be eligible if: 

• You have been accepted to a full-time taught master's course at University of Galway,
• You have attained a first class honours (or equivalent) in a Level 8 primary degree.  

An application for the scholarship scheme is required (separate to the application for a place on the programme). The application portal for 2025 is now open and available here. Applications will close on the 30th September 2025. Full details available here.  

Global Scholarships

University of Galway offers a range of merit-based scholarships to students from a number of countries outside of the EU. Visit here for schemes currently available. 

Application Process 

Students applying for full time postgraduate programmes from outside of the European Union (EU), You can apply online to the University of Galway application portal here.  

Our application portal opens on the 1st October entry year for each the following September. 

Further Information 

Please visit the postgraduate admissions webpage for further information on closing dates, documentation requirements, application fees and the application process.