ANU Micro-credentials (Learning from Data in Clinical Research using R)

Welcome to ANU Micro-credentials

Description 

This micro-credential builds upon data manipulation and visualisation skills to cover statistical modelling and statistical inference, using health and environmental data as examples. 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 enrollees will learn good practice through the creation of a reproducible analysis workflow using Rmarkdown.

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Explore datasets within the R environment
  2. Apply statistical models to infer associations between variables
  3. Interpret and presents the results of an analysis
  4. Build a data analysis workflow

Indicative assessment 

Enrollees 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
  • Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 9

Contact: Professor Eric Stone

 

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