Hobart
Introduction
The GeoData Analytics course will be to provide industry-based geoscientists with an understanding of the fundamental concepts of database handling and manipulation, statistical analyses, pattern recognition and machine learning for the processing, analysis and modelling of large volumes of multivariate geoscience data. This course will focus on rigorous approaches of the above methods for extracting and visualising meaningful information from geochemical, geophysical and geological information with applications for mineral exploration; ore extraction and processing; and waste management. The unit volume of learning consists of 150 hours of assessment, accompanied by 75 hours of taught content, either online and/or in person.
Summary 2021
| Unit name | GeoData Analytics |
|---|---|
| Unit code | KEA713 |
| Credit points | 25 |
| Faculty/School | College of Sciences and Engineering School of Natural Sciences |
| Discipline | CODES ARC |
| Coordinator | Matthew Cracknell |
| Level | Postgraduate |
| Available as student elective? | No |
| Breadth Unit? | No |
Availability
Note
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
Special approval is required for enrolment into TNE Program units.
TNE Program units special approval requirements.
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
Learning Outcomes
1 | Describe methods for the preparation and processing of geoscience data for multivariate analysis. |
2 | Develop workflows for semi-automated and repeatable analysis and modelling. |
3 | Explain how pattern recognition and machine learning can be used for computer-assisted interpretation and inference in the minerals industry. |
4 | Analyse geoscience data to identify previously unrecognised relationships and/or patterns to address mineral industry problems. |
5 | Communicate data analytics results and models using a variety of media to an audience with and without geoscience domain expertise. |
Fees
Teaching
| Teaching Pattern | TBA |
|---|---|
| Assessment | AT1 5 x online quizzes (20%) AT2 Assignment = report (25%) AT3 Assignment - report and seminar(40%) AT4 Online discussion posts - literature review and presentation (15%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required | None |
|---|
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.