Workshops will take place during the Semester 1 Teaching Break and will start at 9:30am, finish at 4:30pm with lunch break 12:30 – 1:30.
You will need to bring your laptop. We have a few laptops which we can lend for the course of the workshops. Please inquire ahead.
To sign up please email firstname.lastname@example.org no later than March 17th specifying the workshop(s) you would like to attend. The workshops are offered at no cost. The number of places is limited, we will be giving priority to the Honours/Master and PhD students and postdocs.
1. Demystifying Linux – Using The Unix Shell 1 day: Monday, 3rd April
Delivered by Bob Buckley and Cameron Jack from the ABC
- Introduction Unix/Linux operating system.
- File system and directory structure.
- Use of the terminal (command line interface).
- Basic Linux shell (bash) commands and tools.
- Remote computing - remote login and file transfer.
2. Introduction to NCI (National Computational Infrastructure) and use of the distributed-memory cluster Raijin 1 day: Tuesday, 4th April
Delivered by an instructor from the NCI
- How to get allocation and start using the machine.
- Logging in, accounting and monitoring usage.
- Job submission and scheduling.
- File system.
- Software environment.
3. Introduction to data processing in R 2 days: Wednesday – Thursday, 5 – 6th April
Delivered by Marcin Adamski from the CBBU
- Introduction to R and RStudio.
- Data types in R, and type conversions.
- Loading and saving tab and coma delimited files.
- Basic data exploration including plotting.
- Data sub-setting, filtering and joining.
- Adding new columns with calculated values.
- Detecting and handling errors an missing values in the data.
- Designing an appropriate structure for a spreadsheet.
- Programming - creating functions in R.
4. Experimental Design and Statistical Data Analysis 3 days: Tuesday – Thursday, 11 – 13th April
Delivered by Terry Neeman from the SCU
Please note that this workshop requires basic knowledge of R. If you are not sure about your skills please first come to the workshop #2 - 'Introduction to data processing in R'.
- Structure of an experiment: response, treatment factors, blocking factors.
- Introduction to the Statistical Model: Mean Structure (including Factorial models), Variance (blocking) structure.
- Planning your experiment: sample size estimation, identifying sources of variation, randomised complete Block, Split Plot, incomplete block designs.
- Data collection and Data Analysis: Exploring data (graphics and data summaries), review of data structures, linear models.
- Practice in fitting linear and generalised linear mixed models to data using R. Interpreting and assessing model assumptions.
- If time permits: Multiple hypothesis testing: false discovery rate and statistical models for genomic data analysis.