- Code EMSC8023
- Unit Value 6 units
- Offered by Research School of Earth Sciences
- ANU College ANU Joint Colleges of Science
- Course subject Earth and Marine Science
- Areas of interest Earth and Marine Sciences, Algorithms and Data, Earth Physics, Geology, Environmental Science
- Academic career PGRD
- Prof Malcolm Sambridge
- Mode of delivery In Person
Second Semester 2023
See Future Offerings
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 be; Monte Carlo methods and solving problems with random numbers, bootstrapping, fitting parameters and probabilistic inference. Students will complete an individually-designed assignment tailored to their specific interests. The experience gained in this course will help students approach their own research.
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.
- A written report detailing their project work, solution to given problems and ideas for how their work can be extended in the future. This report should evaluate the data science techniques on which their project focused and provide critical insights into the capabilities and shortcomings of different approaches (70) [LO 1,2,3,4]
- 15 minute oral presentation to communicate to their peers the problem addressed in their project and their solutions to the tasks they were set. Students will be expected to answer questions about their project and provide a clear justification for choices they made in selecting and applying given techniques to their problem (30) [LO 4,5]
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35 hours of lectures accompanied by 30 hours of workshops focusing on individual projects. Students will be required to complete approx. 65 hours of self-study, based on a combination of class assignments and individual project work.
Not yet determined
Requisite and Incompatibility
A reading list will be provided during the course. Specific reading lists will be developed for individual students based on their tailored projects.
Assumed KnowledgeBasic knowledge of Mathematics
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:
- 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.
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Offerings, Dates and Class Summary Links
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|6075||24 Jul 2023||31 Jul 2023||31 Aug 2023||27 Oct 2023||In Person||N/A|