COURSE CURRICULUM

The curriculum for the postgraduate program in Applied Clinical Data Analytics is structured over one academic year and requires students to complete 90 ECTS credits through a series of required modules. Curriculum information relates to the current academic year and module offerings may be subject to change. Students must successfully complete all required modules to achieve the total credit requirement. Each module represents an examinable component of the course, involving lectures, practical sessions, assignments, and applied analytical work designed to build progressive competence in clinical data analysis.In Semester 1, students establish foundational knowledge through Clinical Data Analytics I, Health Research Methodology I, and Applied Medical Statistics I, each worth 10 credits. These modules introduce core principles of healthcare data analysis, research design, statistical reasoning, and methodological frameworks necessary for interpreting clinical information. This foundation prepares students to critically evaluate data and apply appropriate analytical techniques within healthcare contexts.In Semester 2, the curriculum advances to more complex and applied topics. Clinical Data Analytics II and Applied Medical Statistics II deepen statistical modelling, data management, and analytical interpretation skills. Health Research Methodology II further strengthens research planning, implementation, and critical appraisal competencies. A major component of the program is the Clinical Data Analysis Thesis, divided into Part I (10 credits) and Part II (20 credits). This substantial research project allows students to work with real-world clinical datasets, applying the knowledge and skills acquired throughout the program to address meaningful healthcare questions.Overall, the structured progression from foundational theory to advanced application and independent research ensures that graduates are well equipped to analyse complex healthcare data, contribute to evidence-based practice, and support data-driven decision-making in clinical and health service environments.