- Class Number 8755
- 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 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.
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 interactive Python-based processing examples (no previous knowledge of Python is assumed). We will access Python via the RSES server, so you don't need to install any specific software for the course.
Please see Wattle for more information.
The course will assume some basic knowledge of maths and statistics. Introductory material is provided on the course Wattle page, please review this before the start of the course.
A reading list will be provided with the course handout.
Staff FeedbackStudents will be given feedback in the following forms in this course:
- Written comments
- Verbal comments
- Feedback to the whole class, to groups, to individuals, focus groups
Student FeedbackANU 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 4: Introduction to Data Science & Hypothesis Testing|
|2||Week 5: Correlation and Regression & Error Analysis|
|3||Week 6: Multivariate Statistics|
|4||Week 7: Working with Random Numbers|
|5||Week 8: Monte Carlo Techniques|
|6||Week 9: Bayesian Statistics|
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Homework Assignment 1||50 %||25/10/2019||08/11/2019||1,2,3,4,5|
|Homework Assignment 2||50 %||25/10/2019||08/11/2019||1,2,3,4,5|
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
PoliciesANU 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 RequirementsThe 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 Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.
Moderation of AssessmentMarks 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 lectures, practicals and assessments.
See details of Homework Assignments.
Assessment Task 1
Learning Outcomes: 1,2,3,4,5
Homework Assignment 1
This is an individual homework assignment that will involve processing a real data set using the techniques discussed in Weeks 4, 5 & 6 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.
Assignments should be submitted via the class Wattle page.
Assessment Task 2
Learning Outcomes: 1,2,3,4,5
Homework Assignment 2
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.
Assignments should be submitted via the class Wattle page.
Academic IntegrityAcademic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community 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 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 University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.
Online SubmissionThe 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.
Hardcopy SubmissionFor 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 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.
Referencing RequirementsAccepted 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.
We aim to provide assignment grades approximately 2 weeks after submission. Specific feedback on individual assignments can be requested from the Convenor.
Extensions and PenaltiesExtensions 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 policyAcademic 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 studentsThe 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