MSc Health Data Science
Course Overview
This MSc in Health Data Science is a conversion program designed to train the next generation of specialists to meet the high industry demand for experts who can translate complex health data into actionable insights. The course is tailored for both quantitative graduates and those from healthcare backgrounds, providing flexible pathways to develop essential statistical and computing skills. Your learning culminates in a capstone research project, preparing you for a successful career at the forefront of healthcare innovation.
Key Program Highlights
- Learn from a multidisciplinary team of experts including statisticians, clinicians, epidemiologists, and industry professionals.
- Master in-demand skills in statistical modeling, machine learning, and the analysis of diverse health data sources.
- Tailor your learning to your background and career goals through a flexible, customizable curriculum.
- Gain practical, work-ready experience through an interdisciplinary capstone project with an academic or industry focus.
- Understand the full data lifecycle, from study design and ethics to interpreting and translating research findings.
Course Overview
This MSc in Health Data Science is a conversion program designed to train the next generation of specialists to meet the high industry demand for experts who can translate complex health data into actionable insights. The course is tailored for both quantitative graduates and those from healthcare backgrounds, providing flexible pathways to develop essential statistical and computing skills. Your learning culminates in a capstone research project, preparing you for a successful career at the forefront of healthcare innovation.
Key Program Highlights
- Learn from a multidisciplinary team of experts including statisticians, clinicians, epidemiologists, and industry professionals.
- Master in-demand skills in statistical modeling, machine learning, and the analysis of diverse health data sources.
- Tailor your learning to your background and career goals through a flexible, customizable curriculum.
- Gain practical, work-ready experience through an interdisciplinary capstone project with an academic or industry focus.
- Understand the full data lifecycle, from study design and ethics to interpreting and translating research findings.
Requirements
Modules
- Applied Statistics I
- Applied Regression Models
- Statistical Modelling
- Introduction to Bayesian Modelling
- Mathematical Molecular Biology I
- Discrete Mathematics
- Linear Algebra I
- Numerical Analysis I
- Geometric Foundations of Data Analysis I
- Introduction to the Ethical and Regulatory Frameworks of Clinical Research
- Economics of Health and Health Care
- Economic Evaluation in Health Care
- Database Systems I
- Machine Learning
- Principles of Machine Learning
- Databases
- Health Data Science Research Project
- Statistical Computing for Biomedical Data
- Clinical Research Design
- Statistics for Health Data Science
- Applied Statistics II
- Mathematical Molecular Biology II
- Introduction to Bioinformatics (Honours)
- The Meaning of Life: Bioinformatics
- Linear Algebra
- Numerical Analysis II
- Geometric Foundations of Data Analysis II
- Networks
- Systematic Review Methods
- Data Visualisation
- Modern Statistical Methods
- Causal Inference
- Advanced Statistical Computing for Biomedical Data
- Statistical Modelling for Health Data Science
- Predictive Modelling and Statistical Learning
