• 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 UGRD
  • Course convener
    • Prof Malcolm Sambridge
  • Mode of delivery In Person
  • Offered in First Semester 2020
    Second Semester 2020
    See Future Offerings

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 real 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:

  1. Understand the theoretical and practical aspects of a suite of statistical techniques employed commonly in quantitative Earth Science research.
  2. Evaluate Earth Science data sets in a critical manner using appropriate analysis techniques.
  3. Assess the quality of data needed to obtain specific goals.
  4. Critique the advantages, disadvantages and applicability of data science techniques to a range of problems in the Earth Sciences.
  5. Communicate effectively a variety of data science tools as applicable to Earth Science research problems.

Indicative Assessment

  1. 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]
  2. 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. (null) [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

To enrol in this course you must be enrolled in POTE-HSPC or EMSC-HSPC Honours specialisation. Incompatible with EMSC4020, EMSC8024 or EMSC8023.

Prescribed Texts

A reading list will be provided during the course.

Assumed Knowledge

Basic knowledge of Mathematics

Fees

Tuition fees are for the academic year indicated at the top of the page.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
2
Unit value:
6 units

If you are an undergraduate student and 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). You can find your student contribution amount for each course 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
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
3733 24 Feb 2020 02 Mar 2020 31 Mar 2020 29 May 2020 In Person N/A

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
8743 27 Jul 2020 03 Aug 2020 31 Aug 2020 30 Oct 2020 In Person N/A

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