Semester 1 2023
- Epidemiology (EPI) – will be distributed by the relevant university e.g. EPI at UQ
- Mathematical Foundations for Biostatistics (MFB)
- Principles of Statistical Inference (PSI)
- Data Management and Statistical Computing (DMC)
- Regression Modelling for Biostatistics 1 (RM1)
- Regression Modelling for Biostatistics 2 (RM2)
- Clinical Biostatistics (CLB)
- Health Indicators and Health Surveys (HIS)
- Longitudinal and Correlated Data (LCD)
- Survival Analysis (SVA) – legacy students only
- Probability and Distribution Theory (PDT) – legacy students only
Semester 2 2022
- Epidemiology (EPI) – will be distributed by the relevant university. E.g. EPI at UQ
- Mathematical Foundations for Biostatistics (MFB)
- Regression Modelling for Biostatistics 1 (RM1)
- Regression Modelling for Biostatistics 2 (RM2)
- Principles of Statistical Inference (PSI)
- Design of Randomised Controlled Trials (DES)
- Data Management and Statistical Computing (DMC)
- Bayesian Statistics (BAY)
- Clinical Biostatistics (CLB)
- Causal Statistical Inference (CSI)
- Machine Learning for Biostatistics (MLB)
- Categorical Data and Generalised Linear Models (CDA) – legacy students only
Semester 1 2022
- Epidemiology (EPI) – will be distributed by the relevant university e.g. EPI at UQ
- Mathematical Foundations for Biostatistics (MFB)
- Data Management and Statistical Computing (DMC)
- Principles of Statistical Inference (PSI)
- Regression Modelling for Biostatistics 1 (RM1)
- Regression Modelling for Biostatistics 2 (RM2)
- Health Indicators and Health Surveys (HIS)
- Longitudinal and Correlated Data (LCD)
- Survival Analysis (SVA) – legacy students only
- Probability and Distribution Theory (PDT) – legacy students only
Semester 2 2021
- Epidemiology (EPI) – will be distributed by the relevant university. E.g. EPI at UQ
- Probability and Distribution Theory (PDT)
- Data Management and Statistical Computing (DMC)
- Principles of Statistical Inference (PSI)
- Linear Models (LMR)
- Design of Randomised Controlled Trials (DES)
- Categorical Data Analysis and Generalized Linear Models (CDA)
- Statistical Genomics (SGX)
- Causal Statistical Inference (CSI)
- Machine Learning for Biostatistics (MLB)
