Overview
The Master of Science in Biostatistics and Biomedical Data Science (MSc BBDS) combines rigorous training in biostatistics, data science and AI with a strong focus on biomedical research and healthcare. It equips students with the methodological and analytical skills to address complex challenges across the clinical and biomedical data spectrum.
The programme offers two tracks—Biostatistics and AI, and Bioinformatics and AI—providing specialised training in diverse domains of biomedical science. Courses are taught by leading faculty and data science practitioners from Duke-NUS Medical School and the National University of Singapore (NUS), combining methodological expertise with real-world biomedical applications.
Learning Outcomes
Apply Advanced Data Science Methods
Apply statistical, machine learning, and computational biology methods to analyse clinical and biomedical data for actionable insights.
Integrate Interdisciplinary Knowledge
Integrate knowledge from medicine, biology, and quantitative methods to address complex problems in translational biomedical and clinical research.
Communicate Effectively and Responsibly
Communicate analytical findings clearly to diverse stakeholders while applying ethical principles to the use of biomedical data and AI.
Admissions Requirements
Who should apply
Our program welcomes local and international applicants with strong quantitative skills and an interest in biomedical and healthcare applications.
Programme Structure
Two specialised tracks:
📊 Biostatistics and AI
Focuses on biostatistical methods, machine learning, and AI applied to clinical, patient-level, and population-level data to improve patient care, optimize clinical trials, and strengthen health interventions.
🧬 Bioinformatics and AI
Focuses on computational biology and AI-driven methods to analyse high-throughput genomics and omics data, support drug discovery, and advance precision and personalized medicine.
Courses
Students complete 7 taught courses (4 units each) and a Master’s Thesis (12 units)
Core courses
• Core Concepts in Biostatistics
• Machine Learning for Health and Biomedicine
• Applied Biostatistical Methods
• Computing for Biomedical Data Analytics
• Core Concepts in Bioinformatics (Bioinformatics and AI track)
Elective courses - Examples
• Study Designs in Clinical and Population Health Research
• Analysis of Complex Biomedical Data
• Bioinformatics and Omics Data Analysis
• Big-Data Analytics Technology
• Neural Networks and Deep Learning
• Cloud Computing
12-Unit Master’s Thesis
Work with a faculty supervisor to apply advanced statistical and computational methods to address a real-world biomedical or healthcare research problem.