ETC5522 · Advanced data handling

Overview

This unit will develop your skills in working with dates and times, Geospatial data and web data in R.

Offerings

The Handbook publishes no offerings for this unit.

Assessment

The Handbook publishes no assessment items for this unit yet.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. understand the properties of spatio-temporal data
2. effectively wrangle and visualise spatio-temporal data
3. learn how to efficiently work with big data.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.