- Class Number 8261
- Term Code 2960
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- AsPr David Heslop
- AsPr David Heslop
- Class Dates
- Class Start Date 22/07/2019
- Class End Date 25/10/2019
- Census Date 31/08/2019
- Last Date to Enrol 29/07/2019
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 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.
This course will involve in-class problem solving and a range of interactive examples. A strong focus is placed on working with real data.
Additional Course Costs
Examination Material or equipment
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. Please see Wattle for more information.
A basic knowledge of mathematics, Python coding and Jupyter notebooks is assumed.
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
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.
|Week/Session||Summary of Activities||Assessment|
|1||Week 7: Working with Random numbers|
|2||Week 8: Monte Carlo Techniques|
|3||Week 9: Bayesian Statistics||Homework Assessment|
|4||Week 10: Applied Topic Session 1|
|5||Week 11: Applied Topic Session 2|
|6||Week 12: Applied Topic Session 3||Applied Topic Presentation|
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Homework Assignment||50 %||25/10/2019||08/11/2019||1, 2, 3|
|Data Science Project||50 %||25/10/2019||08/11/2019||1, 2, 3, 4|
* 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:
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 ANU Online 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.
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
Learning Outcomes: 1, 2, 3
This is an individual homework assignment that will involve processing a real data set using the techniques discussed in Weeks 7, 8 & 9 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
Learning Outcomes: 1, 2, 3, 4
Data Science Project
In Weeks 10, 11, 12 you will work on an applied Data Science project. Your individual project will provide you with an opportunity to work with an RSES academic and to explore machine learning ideas through both independent and peer-to-peer learning. Your project will be assessed via a final presentation, where you will have the opportunity to show the problems you addressed in your project and the solutions you developed. Marks will also be awarded based on your ability to answer audience questions.
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.
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.
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 permitted. Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.
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.
Assignment grades will be uploaded to the class Wattle page by the date indicated 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. The Course Convener may grant extensions 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.
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 Diversity 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
Palaeomagnetism, Data Science
AsPr David Heslop