Description 

This micro-credential builds upon data manipulation and visualisation skills to cover statistical principles of good study design and data analysis using statistical modelling in the context of clinical research. Enrollees will gain experience exploring patterns in data and inferring relationships between variables. Common misuses of data analysis, including “p-hacking” and overfitting, will be discussed in depth. The course will emphasise the importance of reproducible analyses, and enrolees will learn good practice through the creation of a reproducible analysis workflow using Rmarkdown.

Learning outcomes 

Upon successful completion, enrolees will have the knowledge and skills to:

1. Explore datasets within the R environment
2. Apply statistical models to infer treatment effects in a randomised controlled clinical trial
3. Interpret and presents the results of data analyses
4. Apply principles of good study design in clinical research
5. Build a data analysis workflow

Indicative assessment 

Enrolees will critically assess a published work for which the data has been made available. They will import the accompanying data into R, and demonstrate they can interpret the main findings, and reproduce aspects of the analysis (approximately 1000 words total).

Assumed knowledge 

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as a stand-alone course or as part of a stack including:

Escape from Excel: Data Wrangling and Visualisation in the Health and Environmental Sciences using R

Details 

Course Code: DATA08

Workload: 21 hours 

• Contact hours: 7 hours: In person sessions: 9, 16, 23 September 2 - 4.30pm
• Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 9

Contact: Dr Terry Neeman and Professor Eric Stone

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

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