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 and explain 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 the foundations of specific data science tools to Earth Science research problems.
Indicative Assessment
- Homework Data set Processing Assignment 1, includes a presentation (50) [LO 1,2,3,4,5]
- Homework Data set Processing Assignment 2, includes a presentation (50) [LO 1,2,3,4,5]
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Workload
The expected workload will consist of approximately 130 hours throughout the semester including:
- Face-to-face component which consists of 4 x 3-hour seminars/workshops for six weeks.
- Approximately 58 hours of self-directed study which will include preparation for the seminars, presentations, and other assessment tasks.
Inherent Requirements
No specific inherent requirements have been identified for this course
Requisite and Incompatibility
Prescribed Texts
A reading list will be provided during the course. Specific reading lists will be developed for individual students based on their tailored projects.
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 |
|---|---|
| 2024 | $4440 |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $6360 |
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 |
|---|---|---|---|---|---|---|
| 7819 | 22 Jul 2024 | 29 Jul 2024 | 31 Aug 2024 | 25 Oct 2024 | In Person | N/A |
