• Class Number 8743
  • Term Code 3060
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Prof Malcolm Sambridge
    • Prof Malcolm Sambridge
  • Class Dates
  • Class Start Date 27/07/2020
  • Class End Date 30/10/2020
  • Census Date 31/08/2020
  • Last Date to Enrol 03/08/2020
SELT Survey Results

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


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.

Research-Led Teaching

This course will involve in-class problem solving and a range of interactive examples. A strong focus is placed on understanding the basics of which method to use and how to solve problems of estimation and uncertainty.

Field Trips

Not applicable

Additional Course Costs

Not applicable

Examination Material or equipment

Laptop for use on RSES Python-based server, or with Jupyter notebook python software installed for offline work.

Required Resources

Bring a laptop for working through interactive Python-based processing examples. We will access Python via the RSES server, so it is not necessary to install any specific software for the course. The server will be available both within and external to the ANU. Although students may use a jupyter python server installed on their local machine if they wish. In that case we advise you download and install Anaconda jupyter prior to course commencement.

A basic knowledge of mathematics, Python coding and Jupyter notebooks is assumed. All enrolled students will have previously taken EMSC4033/EMSC8033 and Introduction to python, which will be sufficient.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Class Schedule

Week/Session Summary of Activities Assessment
1 Hypothesis testing
2 Correlation and Regression Associated python based in class practicals (non assessed)
3 Analytical Error propagation Associated python based in class practicals (non assessed)
4 Cluster Analysis & Dimension reduction Associated python based in class practicals (non assessed)
5 Mid Semester break
6 Introduction to linear and nonlinear parameter estimation
7 Parameter fitting, goodness of fit, and uncertainty -I Associated python based in class practicals (non assessed)
8 Parameter fitting, goodness of fit, and uncertainty -II Associated python based in class practicals (non assessed)
9 Monte Carlo error propagation and Bootstrap methods Associated python based in class practicals (non assessed)
10 Parameter search in weakly and strongly nonlinear problems Associated python based in class practicals (non assessed)
11 Bayesian Inference and Monte Carlo Sampling Associated python based in class practicals (non assessed)

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Take home assignment 1 50 % 21/09/2020 05/10/2020 1,2,3,4,5
Take home assignment 2 50 % 30/10/2020 03/12/2020 7,8,9,10,11

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using 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. For additional information regarding Turnitin please visit the Academic Integrity . In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 50 %
Due Date: 21/09/2020
Return of Assessment: 05/10/2020
Learning Outcomes: 1,2,3,4,5

Take home assignment 1

Students will receive feedback from python based practicals during the first thee weeks of the course immediately preceeding the mid-semester break. At the end of the three weeks students will be given a homework assignment that will involve processing a real data set using the techniques discussed in Weeks 1, 2, & 3 of the course. Your assignment should take the form of a Jupyter Notebook that contains both the code you developed to process the data and a detailed Markdown-based explanation of the steps you took and techniques you used. This explanation is a key component of the assignment, which should be detailed and include, for example, the equations you used, a description of the terms included in them, etc.

Assessment Task 2

Value: 50 %
Due Date: 30/10/2020
Return of Assessment: 03/12/2020
Learning Outcomes: 7,8,9,10,11

Take home assignment 2

This is an individual homework assignment that will involve performing a series of tasks on a synthetic data set using the techniques discussed in Weeks 4, 5, & 6 (session 7-10) of the course. Your assignment should take the form of a Jupyter Notebook that contains both the code you developed to process the data and a detailed Markdown-based explanation of the steps you took and techniques you used. This explanation is a key component of the assignment, which should be detailed and include, for example, the equations you used, a description of the terms included in them, etc.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.

The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

  • Late submission is not accepted for take-home examinations.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

Assessment grades will be uploaded to Wattle by the return date stated above. Students are encouraged to request specific assignment feedback from the appropriate member of the teaching team.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

No. Take home assignments may not be re-submitted, but time extension may be granted with permission of teaching team member.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Prof Malcolm Sambridge

Research Interests

data analysis, robust inference in the geosciences

Prof Malcolm Sambridge

By Appointment
Prof Malcolm Sambridge

Research Interests

Prof Malcolm Sambridge

By Appointment

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions