Overview

To view overview information, please select an offering from the drop-down menu above.

Academic contacts

To view unit coordinator information, please select an offering from the drop-down menu above.

Offerings

MURDOCH-S1-FACE2FACE-2025-ONGOING
MURDOCH-S1-INT-2021-2024

To view more information about assessments, contacts and learning activities specific to one of these offerings, please select an offering from the drop-down menu above.

Enrolment rules

Enrolment in Master of Systems Medicine (Research), Graduate Certificate in Systems Medicine, Graduate Diploma in Systems Medicine.

Other learning activities

To view other learning activity information, please select an offering from the drop-down menu above.

Learning activities

To view learning activity information, please select an offering from the drop-down menu above.

Learning outcomes

1.

Explain the algorithm of selected data pre-processing and analysis methods commonly applied for the analysis of omics data

2.

Apply specialised data analysis skills, techniques and appropriate computational tools to handle and prepare raw OMICS data for statistical analyses.

3.

Justify scientific decision-making with respect to study design and rationalize appropriate application of data analysis techniques

Assessments

To view assessment information, please select an offering from the drop-down menu above.

Additional information

Unit content:

This course aims to provide a comprehensive understanding of the data analysis techniques commonly applied for the analysis of omics data. Covered in this unit are:
• Uni and bivariate statistics (e.g., measures of central tendency and variability, measures of association, effect sizes), parametric distributions, data visualisation techniques
• Statistical group comparison with parametric and non-parametric methods, clustering approaches (e.g. hierarchical, nearest neighbour, k-means)
• Variable scalings and transformations
• Least squares techniques, multivariate statistical methods (e.g., principal components analysis, partial least squares, multidimensional scaling)
• Statistical validation techniques