Unit name | Geographic Data Science |
---|---|
Unit code | GEOG30021 |
Credit points | 20 |
Level of study | H/6 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Wolf |
Open unit status | Not open |
Pre-requisites |
GEOG25010 Spatial Modelling 2 |
Co-requisites |
None |
School/department | School of Geographical Sciences |
Faculty | Faculty of Science |
This unit aims to provide students with an understanding of the methods, techniques, concepts, and theoretical-conceptual grounding for modern data science topics. The unit is structured as a methods survey unit, involving instruction in the concepts & theories behind geographical research in data science, as well as its applications in spatial supervised & unsupervised learning methods. The unit will cover three topics selected based on student interest including, but not limited to: (1) multilevel regression models; (2) spatial regression models; (3) local regression models; (4) generalised linear models; (5) spatial anomaly detection; (6) spatial clustering and regionalisation.
On completion of this Unit students will be able to:
The following transferable skills are developed in this Unit:
The unit will be taught through a blended combination of online and, if possible, in-person teaching, including
Coursework (60%) - ongoing practical assignments marked on best 3 out of 4. Practicals will be held regularly, and accumulated material will be submitted for assessment every 2 weeks. [ILOs 1-3]
Take-home assessment (40%) - Over a 72-hour period, students will undertake a short analysis project using one method covered in the course. The example analysis will take the form of a journal article, report, or engineering blog post and will have a maximum length of 1200 words, not including bibliography or technical annexes. [ILOs 1-3]
The reading will focus on selections from books, two articles, as well as course notes written by the tutor. The following resources are an example of the material that will be covered in reading throughout the course: