- Code EMSC4123
- 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
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
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.
- 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]
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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.
Not yet determined
Requisite and Incompatibility
A reading list will be provided during the course.
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.
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- Unit value:
- 6 units
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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|
|6062||24 Jul 2023||31 Jul 2023||31 Aug 2023||27 Oct 2023||In Person||N/A|