Health Data Science (MSc)

(MSc) Health Data Science

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Course Overview
Key Facts
Course Outline
Why Choose This Course?
Course Fees
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Course Overview

Health Data Science is a rapidly growing discipline, where skilled people are in extremely high demand from industry due to data-led innovations across the sector. Health related data are generated from many sources including observational studies, clinical trials, computational biology, medical records, individual monitoring devices, health care claims, genetic and genomic epidemiology, environmental health, sports science and climate change.

The aim of the programme is to train a new generation of world-leading health data scientists with the essential statistical and computing skills needed to become data science specialists in the health care, biopharmaceutical and medical technology sectors.

This conversion MSc can be tailored around your skills, experience and interests to provide flexible opportunities for those with either strong quantitative backgrounds, or those with an interest in Healthcare that wish to develop their data science and data analytics skills. Graduates will develop the key statistical and computing skills needed to design studies, analyse, interpret and translate research findings to evaluate health interventions, services, programmes, and policies.

The modules will be delivered by recognised experts in pure and applied health data science including statisticians, bioinformaticians, clinicians, mathematicians, health economists, epidemiologists and computer scientists and invited speakers from Medtech, Pharma and Data Science.

The programme is tailored to allow a wide range of student backgrounds, but primarily to:

focus skills of those with quantitative backgrounds to needs of the health sector and
provide biomedical, healthcare and related professionals a path to retrain or upskill in data science.

The courses provide a broad range of skills in statistical modelling, machine learning and clinical research. These skills are brought to fruition in an interdisciplinary capstone research project with either an academic or industry focus.

You will learn about sources of health related data, regulatory environment, ethics, the tools and skills to collate and analyse diverse datasets across various health domains, develop work-ready skills, and understand the varied roles of health data scientists. The wide range of backgrounds and experience of students will create a dynamic learning environment where students can collaborate with peers from different domains to build those work ready skills. On completion you will have the core skills and expertise in the use of statistical data science theory, methods and tools for health and related applications.

Data science skills are in unprecedented demand from many industries, particularly in health care. From precision medicine, to next-generation genomics, to individualised monitoring devices the growth in data collection and data-led decision making is revolutionising health care delivery. If you want to be at the forefront of this revolution in health care in Ireland or globally then this is the MSc for you.

Applications and Selections

Applications are made online via the University of Galway Postgraduate Applications System. Selection is based on a combination of the candidate's academic record, CV including research/professional experience and personal statement (see Supporting Documents website). Applicants may be invited to interview.

Who Teaches this Course

Prof Carl Scarrott

PHD, BSc

Established Professor
School of Mathematics, Statistics and Applied Mathematics
National University of Ireland in Galway
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Dr Andrew Simpkin

BA., PhD.

Senior Lecturer
School of Mathematical and Statistical Sciences
University Road
Galway
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Dr Emma Holian

BSc, PhD

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Dr Davood Roshan Sangachin

B.Sc., M.Sc, PhD

Lecturer Above The Bar
School of Mathematical &
Statistical Sciences
NUI Galway
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Dr Pilib Ó Broin

B.A.,M.Sc.,PhD

Lecturer
SCHOOL OF MATHEMATICS, STATISTIC
& APPLIED MATHEMATICS
NUI GALWAY
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Professor Cathal Seoighe

PhD

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Dr Haixuan Yang

MSc, PhD

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Professor John Newell

BSc, MSc, PhD.

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Dr Neil O'Leary

B.Sc.,M.Sc.,PhD

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Requirements and Assessment

Students are formally assessed through a variety of continuous assessment and end-of-semester written examinations. Continuous assessment include written assignments, data analysis projects (including programming), and individual and group presentations. Assessment of the research project includes a literature review and dissertation, as well as an oral presentation.

Key Facts

Entry Requirements

The minimum entry requirement is an upper second class (or equivalent) class honours degree from a recognised University, in a relevant subject (e.g. statistics, health, biological/biomedical sciences, engineering, computer science, psychology) or evidence of achievement at postgraduate level. Performance in the last two years of study will be considered. Students with insufficient quantitative background must pass a preparatory course.

Additional Requirements

Recognition of Prior Learning (RPL)

Duration

1 year, full-time

Next start date

September 2024

A Level Grades ()

Average intake

QQI/FET FETAC Entry Routes

Closing Date

No set closing date. Offers made on a continuous basis.

NFQ level

Mode of study

ECTS weighting

Award

CAO

Course code

MSC-HDS

Course Outline

The learning outcomes for this course include:

Knowledge and experience of Statistical Data Science tools to analyse diverse health related data;
Understanding the various sources of health related data and sampling methods for data collection in clinical research;
The ability to collate, clean and wrangle large diverse health related datasets;
Being able to identify the appropriate analysis needed and be able to write a statistical analysis plan;
Developing interdisciplinary work ready skills and the ability to communicate results effectively;
The opportunity to develop bespoke data science skills as needed for career development.

The 90 ECTS programme will have:

(a) 60 ECTS of taught modules (30 are core and 30 from electives) over both semesters; and

(b) a 30 ECTS research project

The core taught modules are Statistics for Health Data Science, Statistical Computing for Biomedical Data, Clinical Research Design, Statistical Modelling in Health Data Science, Predictive Modelling and Statistical Learning and Causal Inference. The elective modules provide for a broad range of interests across statistical data science, mathematics, bioinformatics, computer science, ethics, health and health economics.

Full-time students will complete the taught modules over Semesters 1 and 2, and complete the research project by the end of the term.

A range of assessment styles will be used across the taught modules, including assignments, individual and group projects, presentations and exams. A key focus will be on the development of work-ready skills for the modern workplace, including assessments using dynamic reproducible research documents/presentations. The development of team working skills, integral to this interdisciplinary field, will be achieved through group project work.

Following the semester two exam period, students will work full-time on their individual or group research project. At the end of the term, each student will submit a health data science research dissertation and give an oral presentation.

Curriculum Information

Curriculum information relates to the current academic year (in most cases).
Course and module offerings and details may be subject to change.

Glossary of Terms

Credits: 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.
Module: 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.
Subject: 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.
Optional: A module you may choose to study.
Required: 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.
Semester: 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)

Why Choose This Course?

Career Opportunities

Data science skills are in unprecedented demand from many industries, particularly in Healthcare. From precision medicine, to next generation genomics, to individualised monitoring devices the growth in 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, interpret and translate research findings to evaluate health care interventions, services, programmes, and policies.

The graduates of this programme will be equipped with a skill set that is in high demand in the Healthcare, Pharma, MedTech, Biomedical, Sport Science and Health Insurance sectors as well as in academia.

Who’s Suited to This Course

Learning Outcomes

Transferable Skills Employers Value

Work Placement

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€8,890 p.a. (including levy) 2024/25

Fees: Tuition

€8,750 p.a. 2024/25

Fees: Student levy

€140 p.a. 2024/25

Fees: Non EU

€26,000 p.a. (€26,140 p.a. including levy) 2024/25

Postgraduate students in receipt of a SUSI grant—please note an F4 grant is where SUSI will pay €4,000 towards your tuition (2024/25). 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.

Postgraduate fee breakdown = Tuition (EU or NON EU) + Student levy as outlined above.

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