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Applied Clinical Data Analytics (MSc)
Course Overview
The taught postgraduate course in Applied Clinical Data Analytics employs a unique and innovative spiral curriculum, designed specifically for training healthcare professionals in analysis of healthcare data. Domain experts in Clinical Data Analytics from the College of Medicine, Nursing and Health Sciences will deliver the program. Assignments are all real-world examples of clinical research including clinical trials, systematic reviews, observational research, and data from administrative clinical datasets.
- The course is designed to train healthcare workers without a background in data analytics, statistics, or computer programming, to analyse and interpret healthcare data.
- Applies research and data analytics knowledge to clinical and health data to effectively answer research questions.
- We will teach students to understand and learn how to apply traditional statistical techniques and machine learning by completing weekly assignments and an end of year thesis.
- Students will learn data import, cleaning, exploration and analysis using the R programming language and R packages for data analysis, machine learning and source control using GitHub.
- We will teach research methodology and appropriate statistical analysis using R for randomised controlled trials, systematic reviews, case control studies and prospective cohort studies.
Applications and Selections
Course applications are then made online via the University of Galway Postgraduate Applications System.
Who Teaches this Course
1. Spotlight on Staff
Introducing some of the teaching staff of the Applied Clinical Data Analytics Masters programme: https://stories.universityofgalway.ie/introducing-our-teaching-team-acda/index.html
Team Announcement
We are delighted to welcome Honor Griffin to her role as Innovation and Research Programme Officer supporting the MSc in Applied Clinical Data Analytics. Honor also works with the MSc in Clinical Research and with the Neurovascular Cluster in the HRB CRFG.
Honor has diverse experience in project and people management, administration, research, community engagement, innovation, and policy and strategy development across Industry, Social Enterprise, and Community sectors. Honor obtained her BA in Public and Social Policy in 2004 and a Masters in Community Development in 2006 from the University of Galway.
The Future of AI in Medicine
AI has massive potential to revolutionise and better medical care. However, it is not without its risks. In this TEDx Talk Dr Conor Judge,Co-Director & Senior Lecturer, Applied Clinical Data Analytics, and Consultant Nephrologist at Saolta University Health Care Group highlights what is needed in order to implement multimodal AI safely into the healthcare system.
View the Ted Talk here
The message from the Co-directors, and testimonials can go underneath the
2. A message from Co-Directors
Dr Conor Judge and Dr Sonja Khan and Team;
The role of clinical health data has become increasingly more important, offering insight into patient care, operational efficiency, and clinical research. Through this postgraduate taught Master’s programme we will equip healthcare professionals with health data analytics and research methodology skills to navigate this evolving landscape with confidence and knowledge. Students will learn how to ask and answer a research question using health data, code using the R programming language, the fundamentals of research methodology, an applied understanding of medical statistics, and how to create publication ready tables and figures. We are looking forward to welcoming our second student cohort in September 2024, the team are committed to fostering an inclusive and supportive learning environment.
3. Healthcare Centre Awards Finalist 2024
The Applied Clinical Data Analytics Masters was a 2024 finalist in the Irish Healthcare Centre Awards in Education, Learning & Development. This recognition is a testament to the hard work, dedication, and innovative spirit of every member of our team. Being named a finalist reaffirms our commitment to excellence in training healthcare professionals in the analysis of healthcare data.
Requirements and Assessment
Assessment is via a combination of continuous assignments (70%) and written exams (30%), that will be 100% computer-based assessment.
Key Facts
Entry Requirements
- Applicants must hold a healthcare related undergraduate degree with a minimum of 2nd Class Honours.
- A minimum overall score of 7.0 IELTS, with no less than 6.5 in any one band.
Additional Requirements
Recognition of Prior Learning (RPL)
Duration
1 year, full-time (12 months); 2 years, part-time (24 months).
Next start date
September 2024
A Level Grades ()
Average intake
15
QQI/FET FETAC Entry Routes
Closing Date
Please view the offer rounds website.
NFQ level
Mode of study
ECTS weighting
90
Award
CAO
Course code
ACA1; ACA2
Course Outline
University of Galway have launched a graduate course in Applied Clinical Data Analytics that employs a unique and innovative spiral curriculum, designed specifically for training healthcare professionals in analysis of clinical data.
The programme will be delivered by domain experts in Clinical Data Analytics from primarily within the School of Medicine and wider College of Medicine, Nursing and Health Science. Working within a dynamic active learning environment on clinical data will facilitate the development of profession appropriate, individual, collective and inter-professional data skills. Assignments are all real-world examples of clinical research including clinical trials, systematic reviews, observational research, and data from administrative clinical datasets.
Full-time students must complete six core modules (Figure 1) across semester 1 and 2 worth 60 ECTS. Students are also required to complete a Thesis project (30 ECTS) split into two parts. Part-time students complete this program over 2 years, completing 40 ECTS in year 1 and 50 ECTS in year 2 (Figure 2).
Range of Teaching/Formal Learning:
Tutorial-based teaching, flipped classroom, problem based learning, real life case studies will be applied and students will learn by applying statistical tools to work through data sets.
Research project:
Self-directed research project with continuous supervision and feedback.
Figure 2—Term Overview for MSc Applied Clinical Data Analytics (part-time)
Why Choose This Course?
Career Opportunities
- Well positioned to carryout robust data analysis and interpretation of your health data.
- Strengthening opportunities for career progression in medicine, nursing and allied health.
Who’s Suited to This Course
Learning Outcomes
Transferable Skills Employers Value
Work Placement
Study Abroad
Related Student Organisations
Course Fees
Fees: EU
€9,140 p.a. full-time; €4,605 p.a. (including levy) 2024/25
Fees: Tuition
€9,000 p.a. full-time; €4,500 p.a part-time 2024/25
Fees: Student levy
€140 p.a. full-time; €105 p.a. part-time 2024/25
Fees: Non EU
€23,500 p.a. (€23,640 including levy) 2024/25
Find out More
E: clinicaldataanalytics@universityofgalway.ie
Programme directors
Dr Conor Judge,
School of Medicine
E: conor.judge@universityofgalway.ie
Dr Sonja Khan,
School of Medicine
E: sonja.khan@universityofgalway.ie
What Our Students Say
Aoife Boyle | Applied Clinical Data Analytics Student
“Embarking on the Masters course in Applied Clinical Data Analytics has significantly enhanced my understanding of health research and critical analysis. The course has equipped me with the ability to scrutinise complex health data with precision and insight. My theoretical knowledge is now solidly underpinned by practical analytic skills, acquired through hands-on database analytics. I am excited to harness both facets of my skillset to advance my career as a clinical pharmacist in promoting evidence- based medicine in critical care and increase my involvement in the analytics of electronic datasets. “
Dr. Reginald Cadlecott | Anaesthetist and Intensivist Registrar
“As an Anaesthetist and Intensivist Registrar, my engagement with the MSc Clinical Data Analytics program at Galway University has been a profound journey of discovery and empowerment. The decision to enroll was motivated by a keen interest in enhancing my research methodologies and statistical analysis capabilities, which are pivotal in the dynamic and complex field of medical science. This programme exceeded my expectations, offering not just a solid theoretical foundation in clinical research and data analytics but also the practical acumen necessary for immediate application in the intensive care arena. The curriculum is meticulously crafted, integrating clinical practice with analytical science, and it equipped me with the skills to navigate and interpret vast intensivist databases effectively. This newfound proficiency in data analysis has significantly augmented my research activities and informed my clinical decisions. The diversity and expertise of the faculty, encompassing accomplished academicians, seasoned clinicians, and skilled data scientists, provided a rich learning environment. Their insights and guidance encouraged a culture of innovation and critical thinkin
