This course is for Honours students
Data science is the most powerful tool we have for separating scientific fact from fiction. The aim of this course is to provide an advanced background in statistical and computational techniques that are useful in the analysis and characterisation of Earth Science data. A focus will be placed on conceptual understanding of how specific data science techniques work and the situations in which they can and cannot be applied. The course will focus on practical examples, providing the opportunity for hands-on learning through the processing of data sets with Python. Specific topics to be discussed in lectures will include: hypothesis testing, regression, cluster analysis, dimension reduction techniques, error propagation, Monte Carlohods and solving problems with random numbers, bootstrapping, fitting parameters and probabilistic
inference.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Understand the theoretical and practical aspects of a suite of statistical techniques employed commonly in quantitative Earth Science research.
- Evaluate Earth Science data sets in a critical manner using appropriate analysis techniques.
- Assess the quality of data needed to obtain specific goals.
- Critique the advantages, disadvantages and applicability of data science techniques to a range of problems in the Earth Sciences.
- Communicate effectively a variety of data science tools as applicable to Earth Science research problems.
Indicative Assessment
- Take home assignment involving the processing of real data sets using the techniques introduced during the lectures. Students will be required to write a report and give a short presentation outlining their choice of specific techniques and discussing how they addressed the tasks in their assignment. (50) [LO 1,2,3,4,5]
- A one-day data science assignment involving the processing of real data sets. Students will be required to give a short presentation justifying their choice of specific techniques and describing how they addressed the tasks in their assignment. (50) [LO 1,2,3,4,5]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
65 hours of lectures including working through interactive examples. Students will be required to complete approx. 65 hours of self-study based on a combination of assignments.
Inherent Requirements
Not yet determined
Requisite and Incompatibility
Prescribed Texts
A reading list will be provided during the course.
Assumed Knowledge
Basic knowledge of MathematicsFees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2023 | $4320 |
- International fee paying students
| Year | Fee |
|---|---|
| 2023 | $6180 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 6062 | 24 Jul 2023 | 31 Jul 2023 | 31 Aug 2023 | 27 Oct 2023 | In Person | N/A |
