Units

The BCA Program has 3 course options and is comprised of 17 units or individual subjects.

Semester
Unit of Study
Course Requirement
On completion of this unit students should be familiar with the major concepts and tools of epidemiology, the study of health in populations, and should be able to judge the quality of evidence in health-related research literature.

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COORDINATORS:
A/Prof Lisa Hall University of Queensland, School of Public Health Semester 1, 2
PREREQUISITES: Epidemiology, Mathematical Background for Biostatistics
To enable students to understand and apply the principles of design and analysis of experiments, with a particular focus on randomised controlled trials (RCTs), to a level where they are able to contribute effectively as a statistician to the planning, conduct and reporting of a standard RCT.

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COORDINATORS:
Dr Murthy Mittinty University of Adelaide, School of Public Health Semester 2
PREREQUISITES: None
On completion of this unit students will be able to follow the mathematical demonstrations and proofs used in biostatistics at Masters degree level, and to understand the mathematics behind statistical methods introduced at that level. The intention is to allow students to concentrate on statistical concepts in subsequent units, and not be distracted by the mathematics employed.

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COORDINATORS:
Dr Murthy Mittinty University of Adelaide, School of Public Health Semester 1
PREREQUISITES: Mathematical Background for Biostatistics
This unit will focus on applying the calculus-based techniques learned in Mathematical Background for Biostatistics (MBB) to the study of probability and statistical distributions. These two units, together with the subsequent Principles of Statistical Inference (PSI) unit, will provide the core prerequisite mathematical statistics background required for the study of later units in the Graduate Diploma or Masters degree.

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COORDINATORS:
Prof Andrew Forbes Monash University, Department of Epidemiology and Preventive Medicine Semester 1
Dr Jessica Kasza Monash University, Department of Epidemiology and Preventive Medicine Semester 2
PREREQUISITES: None
The aim of this unit is to provide students with the knowledge and skills required to undertake moderate to high level data manipulation and management in preparation for statistical analysis of data typically arising in health and medical research.

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COORDINATORS:
Dr Jennie Louise University of Adelaide, School of Public Health Semester 1
Mr David Fitzgerald University of Queensland, School of Public Health Semester 2
PREREQUISITES: Mathematical Background for Biostatistics, Probability and Distribution Theory
To provide a strong mathematical and conceptual foundation in the methods of statistical inference, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in health research.

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COORDINATORS:
Ms Liz Barnes University of Sydney, NHMRC Clinical Trials Centre Semester 1
Dr Erin Cvejic University of Sydney, Sydney School of Public Health Semester 2
Ms Katrina Blazek University of Sydney, Sydney School of Public Health Semester 2
PREREQUISITES: Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory
CO-REQUISITES: Principles of Statistical Inference
To enable students to apply methods based on linear models to biostatistical data analysis, with proper attention to underlying assumptions and a major emphasis on the practical interpretation and communication of results.

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COORDINATORS:
Dr Timothy Schlub University of Sydney, Sydney School of Public Health Semester 1
A/Prof Stephane Heritier Monash University, Department of Epidemiology and Preventive Medicine Semester 2
PREREQUISITES: Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory, Principles of Statistical Inference
CO-REQUISITES: Linear Models
To enable students to use generalized linear models (GLMs) and other methods to analyse categorical data with proper attention to the underlying assumptions. There is an emphasis on the practical interpretation and communication of results to colleagues and clients who may not be statisticians.

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COORDINATORS:
Dr Michael Waller University of Queensland, School of Public Health Semester 2
PREREQUISITES: Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory, Principles of Statistical Inference, Linear Models
To enable students to analyse data from studies in which individuals are followed up until a particular event occurs, e.g. death, cure, relapse, making use of follow-up data also for those who do not experience the event, with proper attention to underlying assumptions and a major emphasis on the practical interpretation and communication of results.

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COORDINATORS:
A/Prof Jun Ma Macquarie University, Department of Statistics Semester 1
PREREQUISITES: Mathematical Background for Biostatistics
On completion of this unit students should be able to derive and compare population measures of mortality, illness, fertility and survival, be aware of the main sources of routinely collected health data and their advantages and disadvantages, and be able to collect primary data by a well-designed survey and analyse and interpret it appropriately.

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COORDINATORS:
A/Prof Kevin McGeechan University of Sydney, Sydney School of Public Health Semester 1
PREREQUISITES: Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory, Principles of Statistical Inference, Design of Randomised Controlled Trials
CO-REQUISITES: Linear Models
To enable students to use correctly statistical methods of particular relevance to evidence-based health care and to advise clinicians on the application of these methods and interpretation of the results.

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COORDINATORS:
Dr Michael Waller University of Queensland, School of Public Health Semester 1
PREREQUISITES: Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory, Principles of Statistical Inference, Linear Models, Categorical Data and Generalised Linear Models
To enable students to apply appropriate methods to the analysis of data arising from longitudinal (repeated measures) epidemiological or clinical studies, and from studies with other forms of clustering (cluster sample surveys, cluster randomised trials, family studies) that will produce non-exchangeable outcomes.

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COORDINATORS:
Dr John Holmes University of Melbourne, School of Population and Global Health Semester 1
PREREQUISITES: Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory, Principles of Statistical Inference, Linear Models, Categorical Data and Generalised Linear Models
To achieve an understanding of the logic of Bayesian statistical inference, i.e. the use of probability models to quantify uncertainty in statistical conclusions, and acquire skills to perform practical Bayesian analysis relating to health research problems.

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COORDINATORS:
Prof Lyle Gurrin University of Melbourne, School of Population and Global Health Semester 2
PREREQUISITES: Mathematical Background for Biostatistics, Data Management and Statistical Computing, Probability and Distribution Theory, Principles of Statistical Inference, Linear Models
To learn about relevant biology and terminology, to understand the most important mathematical models and inference methods in statistical genetics, to be able to test for association between genetic variants and outcomes of interest, and to use genome-wide statistical models to help understand the genetic mechanisms underlying a trait and to predict outcomes.

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COORDINATORS:
Prof David Balding University of Melbourne Semester 2
PREREQUISITES: Minimum of 4 units, including Linear Models and Data Management & Statistical Computing
The aim of this unit is that the student gains practical experience, usually in workplace settings, in the application of knowledge and skills learnt during the coursework of the masters program.

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PREREQUISITES: Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory, and Linear Models or a multivariable regression unit of study from a Master of Public Health course or equivalent
This unit covers modern statistical methods for assessing the causal effect of a treatment or exposure from randomised or observational studies.

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COORDINATORS:
Prof Lyle Gurrin University of Melbourne, School of Population and Global Health Semester 2
PREREQUISITES: Linear Models or Regression methods for epidemiology (or equivalent unit)
CO-REQUISITES: Categorical Data and Generalised Linear Models
Recent years have brought a rapid growth in the amount and complexity of health data captured, requiring new statistical techniques in both predictive and descriptive learning. Machine learning algorithms for classification and prediction, complement classical statistical tools in the analysis of these data. This unit will cover modern machine learning methods particularly useful for large and complex health data.

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COORDINATORS:
Prof Armando Teixeira-Pinto University of Sydney, Sydney School of Public Health Semester 2