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:

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 with Jupyter notebook python software installed for offline work. If class requests we can also install on RSES Python-based server.

Required Resources

Bring a laptop for working through interactive Python-based processing examples. We can access Python via the RSES server if needed, although it has become common for students to use a jupyter python server installed on their local machine. In that case we advise you download and install a suitable environment for running Jupyter notebooks prior to course commencement. This option allows students to also work off line.

Recommended Resources

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.

Recommended student system requirements 

ANU courses commonly use a number of online resources and activities including:

• video material, similar to YouTube, for lectures and other instruction
• two-way video conferencing for interactive learning
• email and other messaging tools for communication
• interactive web apps for formative and collaborative activities
• print and photo/scan for handwritten work
• home-based assessment.

To fully participate in ANU learning, students need:

• A computer or laptop. Mobile devices may work well but in some situations a computer/laptop may be more appropriate.
• Webcam
• Speakers and a microphone (e.g. headset)
• Reliable, stable internet connection. Broadband recommended. If using a mobile network or wi-fi then check performance is adequate.
• Suitable location with minimal interruptions and adequate privacy for classes and assessments.
• Printing, and photo/scanning equipment

For more information please see https://www.anu.edu.au/students/systems/recommended-student-system-requirements

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). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Hypothesis testing	Assessed in Assignment 1.
2	Correlation and Regression	Associated python based in class practicals. Assessed in Assignment 1.
3	Analytical Error propagation	Associated python based in class practicals. Assessed in Assignment 1.
4	Cluster Analysis & Dimension reduction	Associated python based in class practicals. Assessed in Assignment 1.
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. Assessed in Assignment 2.
8	Parameter fitting, goodness of fit, and uncertainty -II	Associated python based in class practicals. Assessed in Assignment 2.
9	Monte Carlo error propagation and Bootstrap methods	Associated python based in class practicals. Assessed in Assignment 2.
10	Parameter search in weakly and strongly nonlinear problems	Associated python based in class practicals. Assessed in Assignment 2.
11	Bayesian Inference and Monte Carlo Sampling	Associated python based in class practicals. Assessed in Assignment 2.

Assessment Summary

Assessment task	Value	Due Date	Learning Outcomes
Take home assignment 1 (50%)	50 %	13/09/2024	1,2,3
Take home assignment 2 (50%)	50 %	18/10/2024	1,3,4,5

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

Policies

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 Integrity Rule before the commencement of their course. Other key policies and guidelines include:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Special Assessment Consideration Guideline and General Information 
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

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 Skills website. 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.

Participation

All students are expected to participate in the various components of the course. Participation is particularly important when working through the interactive examples on which the course is based.

Assessment Task 1

Take home assignment 1 (50%)

Students will receive feedback from python based practicals during the first thee weeks of the course. 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

Take home assignment 2 (50%)

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. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

 

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

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

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

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).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Access and inclusion for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents undergraduate and ANU College students
• PARSA supports and represents postgraduate and research students