Much of the learning in this course is through practical hands-on use of the R platform for statistics.
There are 6 sets of lab instructions. Two of these are purely learning material and not assessed (weeks 1 and 2). Four are assessed (weeks 3, 4, 5 and 7). Lab sessions are sequenced to run in parallel with the course content and will cover practical topics related to them. Due to the Mātariki holiday this year weeks 1 and will blend into one another in 2023.
Finally, there is a more open-ended mini-project which you will be able to work on in the last weeks of trimester. Working on this will allow you to apply the skills you have learned through the course to complete a more substantial analysis of real slightly ‘messy’ data.
You can get an idea of what’s coming from the links below.
HOWEVER, materials are not current for 2023 until the pages are marked with the heading Geog 315 T1 2023 (and not an earlier year). You are advised not to work on any lab materials until the heading is updated (since the assessment items may change) to 2023. You can still get an idea of what to expect in most cases, before then.
There are two non-assessed lab sessions. In addition, class time in the second half of trimester will aim provide an environment where it is easy to get assistance and advice from the course instructor to support any remaining work on the final assignment, or the mini-project.
You are strongly advised to make the most of these sessions!
The final assessment in the class is a mini-project, which is a more open-ended and more substantial exercise in data analysis and presentation. All class time in the last 3 weeks or so of semester will be allocated to supporting you in this exercise. An assortment of datasets will be provided, from which you choose one to work on. The data will need some tidying and sorting to be suitable for use, and you will be expected to figure out an analysis workflow yourself, to pursue a particular angle of inquiry you are interested in.
A number of datasets are available covering a range of topics, so hopefully you’ll be able to find something of interest. If you have a dataset from some other class or an idea for a topic not in the list of available topics, then please be in touch and we can discuss possibilities.
Expectations for the submitted report from the mini-project are provided here.
A link to join the course slack channel will be sent via Nuku. This should be used for technical support questions on the labs. All are welcome to answer any questions posted there (don’t wait for your instructor!).