Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- The Environment School
Large data sets are becoming increasingly common in both the life and environmental sciences. They are also vital for addressing global challenges such as biodiversity loss, climate change and global pandemics. Consider for example satellite data capturing global coral reef biodiversity, worldwide daily temperatures measured over decades, or genome sequences of thousands of viruses.
This course will provide you with the tools to handle, visualise and analyse such large data sets. Topics covered include data processing (cleaning, exploring & wrangling), an overview of key data types and structures (e.g., spatial, textual, and genomic data), key concepts of data visualisation, an introduction to machine learning, and a primer in simulations. A major focus of this course will be on reproducible research and communication. Throughout the course we will use the programming language R. No prior knowledge of programming or advanced statistics (beyond STAT1201) is assumed.
Course requirements
Assumed background
Basic statistical concepts, as taught for example in STAT1201.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
STAT1201 or equivalent.
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
To equip students with the knowledge, tools and confidence to explore, wrangle, visualise and analyse large biological and environmental datasets.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Import, explore, analyse and visualise complex data sets.
LO2.
Create visualisations that display complex data in creative ways.
LO3.
Apply principles of reproducible data science.
LO4.
Understand basic concepts of machine learning and implement machine learning models in R.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
Quizzes
|
10% |
1) Quiz 1 6/03/2026 2:00 pm 2) Quiz 2 20/03/2026 2:00 pm 3) Quiz 3 2/04/2026 2:00 pm 4) Quiz 4 24/04/2026 2:00 pm 5) Quiz 5 8/05/2026 2:00 pm 6) Quiz 6 22/05/2026 2:00 pm |
| Project |
Project 1
|
40% |
16/04/2026 2:00 pm
To be completed during the Practical in Week 7. |
| Computer Code | Practical Portfolio | 10% Pass/Fail |
29/05/2026 2:00 pm |
| Project | Project 2 | 40% |
12/06/2026 2:00 pm |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Quizzes
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 10%
- Due date
1) Quiz 1 6/03/2026 2:00 pm
2) Quiz 2 20/03/2026 2:00 pm
3) Quiz 3 2/04/2026 2:00 pm
4) Quiz 4 24/04/2026 2:00 pm
5) Quiz 5 8/05/2026 2:00 pm
6) Quiz 6 22/05/2026 2:00 pm
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04
Task description
Biweekly quizzes on Blackboard. Each quiz consists of multiple choice, short answer or similar types of questions relating to the content of the two weeks up to the submission deadline for each quiz. Quizzes will be open from Wednesdays 8am in each respective week, and close at the indicated deadline. Each quiz is worth 2% of your total grade. There will be six quizzes in total, with only the best five counting towards your grade.
The quiz window will remain open until indicated deadline and once you attempt the quiz you will have 15 minutes to complete the quiz.
The use of any materials is not permitted for this quiz.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Mini-quizzes will be online on Blackboard.
Online submission via Blackboard only by the due date and time. Refer to Blackboard for the submission details.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
If you are granted an approved extension to the quiz you will need to contact the Course coordinator to ensure the quiz is available for you to complete by your new approved due date.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date. For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
Project 1
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Project
- Weight
- 40%
- Due date
16/04/2026 2:00 pm
To be completed during the Practical in Week 7.
- Other conditions
- Time limited, Secure.
- Learning outcomes
- L01, L02, L03
Task description
Students will work on a small data science project that covers the material of weeks 1 through 6 of the course.
This assessment task is to be completed in-person. This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
See Additional Course Grading Information for the hurdle information relating to this assessment item. Please note, If you fail this original semester assessment, you will be given the chance to attempt it again. Please note that the maximum mark you can receive from a second attempt is the minimum pass result of 50%.Submission guidelines
The code produced for this project needs to be submitted via Turnitin only at the end of class time.
Online submission by Turnitin only by the due date and time. Refer to Blackboard for the submission link. No hard copy or assignment cover sheets are required. Submission via email is not accepted.
Deferral or extension
You may be able to defer this exam.
If a deferral is approved, students may work on and submit work on an alternative project, under identical conditions.
See the Additional assessment information section further below for information relating to extension and deferral applications.
Late submission
The code produced for this project needs to be submitted via Turnitin only at the end of class time.
Practical Portfolio
- Mode
- Written
- Category
- Computer Code
- Weight
- 10% Pass/Fail
- Due date
29/05/2026 2:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
A portfolio of all the code and reports produced during the practicals.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
This portfolio needs to be submitted during the last week of semester.
Online submission only by the due date. No hard copy or assignment cover sheets required.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
See the Additional assessment information section further below for information relating to extension and deferral applications.
Late submission
This assessment is graded on a pass/fail basis, and there is no system for applying "mark" penalties. If the assessment item is not submitted by the submission due date, it will be considered a non-submission resulting in a zero shown as a "fail" result.
Project 2
- Mode
- Written
- Category
- Project
- Weight
- 40%
- Due date
12/06/2026 2:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
Students will work on data science project that focuses on machine learning but broadly covers the material of the entire course (weeks 1 through 12).
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
The project report needs to be submitted during examination week 1.
Online submission by Turnitin only by the due date and time. Refer to Blackboard for the submission link. No hard copy or assignment cover sheets are required. Submission via email is not accepted.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
See the Additional assessment information section further below for information relating to extension and deferral applications.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date. For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 0% |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 30% |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: The minimum percentage required for this grade is: 45% |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 50% |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 65% |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 75% |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 85% |
Additional course grading information
Assessment Hurdle:
In order to pass this course, you must meet the following requirements (if you do not meet these requirements, the maximum grade you will receive will be a 3):
You must obtain 50% or more on the Project 1 assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Should you fail a course with a grade of 3, you may be eligible for supplementary assessment.
Refer to the link above for information on supplementary assessment and how to apply. Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.
If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met. Supplementary assessment can take any form (such as a written report, oral presentation, examination or other appropriate assessment) and may test specific learning outcomes tailored to the individual student, or all learning outcomes.
To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.
Additional assessment information
Applications for Extensions to Assessment Due Dates
Read the information contained in the following links carefully before submitting an application for extension to assessment due date.
For guidance on applying for an extension, information is available here: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension
For the policy relating to extensions, information is available here (Part D): https://policies.uq.edu.au/document/view-current.php?id=184
Please note the University's requirements for medical certificates here: https://my.uq.edu.au/information-and-services/manage-my-program/uq-policies-and-rules/requirements-medical-certificates
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Additional learning resources information
Students need to bring their laptops to classes.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (23 Feb - 01 Mar) |
General contact hours |
Week 1: Introduction to R and Studio Learning outcomes: L01, L03 |
Week 2 (02 Mar - 08 Mar) |
General contact hours |
Week 2: Working with Data Learning outcomes: L01, L02 |
Week 3 (09 Mar - 15 Mar) |
General contact hours |
Week 3: Data Wrangling Learning outcomes: L01 |
Week 4 (16 Mar - 22 Mar) |
General contact hours |
Week 4: Special Types of Data Learning outcomes: L01 |
Week 5 (23 Mar - 29 Mar) |
General contact hours |
Week 5: Data Visualisation Learning outcomes: L01, L02 |
Week 6 (30 Mar - 05 Apr) |
General contact hours |
Week 6: Visualising Spatial Data Learning outcomes: L01, L02 |
Week 7 (13 Apr - 19 Apr) |
General contact hours |
Week 7: Reproducible Data Science Learning outcomes: L03 |
Week 8 (20 Apr - 26 Apr) |
General contact hours |
Week 8: Simulating Data Learning outcomes: L01, L04 |
Week 9 (27 Apr - 03 May) |
General contact hours |
Week 9: Simulating Time Series Learning outcomes: L01, L04 |
Week 10 (04 May - 10 May) |
General contact hours |
Week 10: Principles of Machine Learning Learning outcomes: L04 |
Week 11 (11 May - 17 May) |
General contact hours |
Week 11: A Tour of Models in Practice Learning outcomes: L04 |
Week 12 (18 May - 24 May) |
General contact hours |
Week 12: Model Tuning and Avoiding Data Leakage Learning outcomes: L04 |
Week 13 (25 May - 31 May) |
General contact hours |
Week 13: Independent Work on Project II and Practical Portfolio Learning outcomes: L01, L02, L03, L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
- Recording of Teaching Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.