Discover how to ask better questions — and find meaningful answers. 

Data science is the most powerful tool we have for separating scientific fact from fiction. In this course, you'll explore practical and relevant problems in Earth and Environmental Sciences by learning how to interpret data, assess uncertainty, model outcomes and make informed decisions. You'll tackle questions like: 

“How can I make predictions of the future?”  

"Where should I expend the most effort to improve my results?”  

“What accuracy and how many measurements do I need?”  

“Is this result meaningful, or just random chance?” 

"Why is this happening and what's driving the change?"

Using real-world examples and hands-on activities, you’ll develop analytical thinking and quantitative problem-solving skills that are highly valued across science and industry. This course provides a foundation in essential tools, including data analysis, modelling and statistics, through engaging, problem-based learning tailored to the challenges Earth and Environmental scientists face today. 

Research-Led Teaching

The activities in this course are designed to reflect how data analysis is carried out in professional scientific and industry workplaces. Early tasks focus on core technical skills and workflow, ensuring students can use tools such as notebooks, version control, and collaboration platforms reliably. Group projects will mirror real team-based research, where individuals take responsibility for specific roles, contribute to a shared outcome, and document their work clearly so others can understand and build on it. A final individual project brings these elements together, requiring students to define a research problem, work through a data analysis protocol, and communicate results to peers.

Field Trips

None

Additional Course Costs

None

Examination Material or equipment

None

Required Resources

None

Recommended Resources

Whether you are on campus or studying online, there are a variety of online platforms you will use to participate in your study program. These could include videos 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/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Exercises will rely on Google Colab and GitHub, therefore access to a laptop is required. Please speak to the course convener if you need to borrow a laptop for the course.

Staff Feedback

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

• written comments
• verbal comments
• feedback to whole class, groups, and individuals.

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	Course introduction and Fermi estimation
2	GitHub and collaboration with Google Colab
3	Data import & visualisation
4	Data, samples, & populations
5	Monte Carlo techniques
6	Estimating probabilities
7	Hypothesis testing
8	Correlation and Regression
9	Numerical simulation and modelling
10	Time series analysis
11	Data Science Fair preparation
12	Data Science Fair

Tutorial Registration

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

Assessment Summary

Assessment task	Value	Due Date	Return of assessment	Learning Outcomes
Assessment 1: Core Skills	15 %	27/03/2026	10/04/2026	2,4,5
Assessment 2: Group Projects	45 %	22/05/2026	05/06/2026	1,2,3,4,5,6,7
Assessment 3: Individual Project	40 %	29/05/2026	12/06/2026	1,2,3,4,5,6,7

* 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
• Extenuating Circumstances Application
• 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 ‘Canvas’ 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

Students are expected to participate in all components of the course. Participation can be in-person and / or reviewing class recordings, however, in-person participation is encouraged because the course will be based on interactive exercises and discussions. Students will also need to participate in group projects as part of Assessment 2. In-person attendance is required for the Week 12 Data Science Fair.

Examination(s)

Not applicable.

Assessment Task 1

Assessment 1: Core Skills

Assessment 1 is a hurdle requirement, meaning it must be completed to pass the course.

The purpose of Assessment 1 is to ensure that you have developed the core technical skills required for the later parts of the course. This assessment is based on task completion, not on code complexity, elegance, or advanced features. You are being assessed on whether you can successfully carry out the required workflow and produce the expected outputs.

To pass the hurdle requirement, all listed tasks must be completed.

Assessment 1 tasks

You must complete all of the following by the Assessment 1 submission deadline (see course schedule).

1) Share your GitHub repository created in EMSC2010_W2_P2, which contains a notebook that:

• was opened in Google Colab directly from GitHub,
• was shared with another member of the class, and
• includes commits to GitHub that incorporate edits made by that other person.

2) Ensure that one member of your group shares the GitHub repository containing a notebook with your group’s Fermi estimation problem undertaken in EMSC2010_W2_P2.

3) Share a GitHub repository corresponding to the work undertaken in EMSC2010_W3_P1, which contains a notebook with your completed plots of the Phyphox magnetometer data and the corresponding Excel data file.

4) Share a GitHub repository corresponding to the work undertaken in EMSC2010_W3_P2, containing a notebook with your completed Cartopy earthquake and plate tectonic boundary plots.

Assessment Task 2

Assessment 2: Group Projects

Assessment 2 is based on your weekly group projects in Weeks 5, 7, 8, 9, and 10. These projects are designed to build skills, accountability, and scientific reasoning. Assessment 2 is graded in two parts. Weekly projects focus on role fulfilment and engagement, while a final group portfolio will be used to assess understanding.

Part 1 - Role fulfilment (15% total across 5 group projects)

Each week, each student will have an agreed project role within their group. You are assessed on whether you fulfil that role and reflect briefly on your contribution. Your role and its completion must be recorded in the group project Colab notebook. Each weekly notebook will include a table listing:

• each group member’s role,
• whether the role was fulfilled, and
• whether deputy intervention was required.

Each student must also include a short reflective statement (one paragraph) in the notebook describing what they did and what they learned.

Each week, the student assigned the GitHub repository and integration lead role must share the private project GitHub repository with the teaching team for grading.

Grades for each weekly project are awarded as follows:

Role fully fulfilled (3%): The student completes all tasks associated with their role and provides a reflective statement.

Role partially fulfilled (1.5%): Deputy intervention was required (this must be briefly documented in the notebook), or a reflective statement was not provided.

Role not fulfilled (0%): Deputy intervention was required and no reflective statement was provided. The reason for deputy intervention must be documented in the notebook.

Part 2 – Understanding and scientific reasoning (30% total across 3 portfolio projects)

Your group will submit a portfolio of three projects, selected from the five completed weekly projects. The submission date is provided on Canvas.

Your group should decide collectively which three projects best represent your work. A form will be provided where you record your chosen projects.

Each portfolio project is graded using the same marking rubric (below), and all students in the group will receive the same mark for each project. If a student made no contribution to a particular project, this must be clearly recorded in the project notebook. In this case, that student will receive a zero for the portfolio grade associated with that project.

Code quality is assessed indirectly, through reasoning, clarity, and reproducibility rather than stylistic perfection (this is not a programming course). A marking rubric is provided on Canvas. The use of AI-generated code is permitted, provided it is understood, appropriately adapted, and acknowledged.

Assessment Task 3

Assessment 3: Individual Project

In Assessment 3, you will undertake an individual data analysis project that addresses a scientific problem of your choosing. This project is designed to demonstrate the skills, understanding, and judgement you have developed throughout the course. Assessment 3 is graded in three parts, which together assess your analytical reasoning, reflection, and ability to communicate your work to others. Marking rubrics are provided on Canvas.

Part 1 - Project notebook and repository (20%)

Your project notebook and associated GitHub repository will be graded using a rubric similar to that used for the group portfolio projects, but applied at an individual level. The marking rubric is provided on Canvas.

Part 2 - Individual reflection (10%)

Your notebook must include a personal reflection of up to 500 words.

This reflection should focus on your decision-making and learning process. Possible topics include:

• your motivation for selecting the project topic,
• unexpected challenges you encountered,
• changes you made to your project due to data or methodological limitations, and
• what you consider to be the key strengths and shortcomings of your final analysis.

There is no single “correct” reflection; marks are awarded for thoughtful and honest engagement.

Part 3 - Data Science Fair (10%)

To assess communication and understanding, the class will participate in a Data Science Fair with peer-to-peer marking. In science and data-driven work, your ideas are constantly reviewed by peers through discussion, collaboration, and feedback. Peer assessment mirrors this process and helps you practise explaining, questioning, and evaluating scientific work constructively. The Data Science Fair will be held in Week 12.

For this component, you must:

• create and print an A3 poster that provides a clear visual summary of your project, and
• prepare a short, laptop-based demonstration of your analysis to explain your work to peers.

A randomly selected group of your peers will assess your project using the rubric below. Multiple peer assessments will be averaged to produce your final mark for this component.

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 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.

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. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Returning Assignments

Assignment grades and feedback will be provided within 10 working days weeks of submission.

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

Resubmission is permitted for Assessment 1. Resubmission is not permitted for Assessments 2 and 3.

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 Accessibility 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 supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students