[closed] Self-paced online course May 22 – July 7 2023

If you’ve missed out, our next iteration is likely to be early 2024! You can fill up this short survey to be notified when we open our next course.

Summary

  • The new course is open and will run for 7 weeks. This course is online, but at your own pace, meaning that you need to dedicate enough time (5-8h per week) to fully benefit from the program.
  • There are 4 weeks of asynchronous learning (you work at our own pace to cover the material).
  • There are 4 live webinars organised on Thursdays at 5pm AEST (convert your time here) in the first 4 weeks to summarise some key concepts and ask your questions (the webinars will be recorded).
  • You will have the opportunity to chat on Slack and ask your questions during the whole course.
  • You can analyse your own data for the assessment (due in week 6) or use the data provided. You will reinforce your learning by marking the assignments of 2-3 other learners.

Feedback from the 2022 iteration can be found here.

  • Teaching Period Dates, asynchronised:
    • Learning Start: Monday, 22 May 2023, 9:00 am AEST
    • Learning Ends: Sunday, 18 June 2023, 11:59 pm AEST (4 weeks)
    • (non marked) Assessment due: Friday 30th June 2023 (2 weeks)
    • Peer-review of assessment due: Friday 7th July 2023 (1 week)
  • Fees vary for
    • Research Higher Degree students enrolled at a University: $495 AUD (incl. GST)
    • Staff and members from Universities & Not-for-profit organisations: $825 (incl. GST)
    • Other industries: $1320 AUD (incl. GST)
    • discounts of 5% for a group of 3-9 learners and 10% for 10+ learners, however, this will require a single invoice per group.

(these funds go towards the support of a software developer to maintain the package)

The number of places is limited, so first come first serve (we aim to run this course twice a year).

What if I need an invoice? Contact Student Support at continuing-education[at]unimelb.edu.au

Prerequisites. A good working knowledge in R programming (e.g. handling data frame, perform simple calculations and display simple graphical outputs) is essential to fully benefit from the course*. The course is divided into theory (50%) and hands-on practice, with the opportunity to analyse your own data. The exercises and assignments are in R. Participants are encouraged to use RStudio and Rmarkdown (template and R code provided).

*Learners who are not proficient in R do not get the full benefit of the course (based on their own, honest, feedback!)

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