Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- 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% |
Quiz 1: 7/03/2025 2:00 pm Quiz 2 : 21/03/2025 2:00 pm Quiz 3: 4/04/2025 2:00 pm Quiz 4: 17/04/2025 2:00 pm Quiz 5: 9/05/2025 2:00 pm Quiz 6: 23/05/2025 2:00 pm |
Project |
Project 1
|
40% |
This project task needs to be completed during the practical session in Week 7. |
Computer Code | Practical Portfolio | 10% Pass/Fail |
30/05/2025 2:00 pm |
Project | Project 2 | 40% |
13/06/2025 2:00 pm |
Assessment details
Quizzes
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 10%
- Due date
Quiz 1: 7/03/2025 2:00 pm
Quiz 2 : 21/03/2025 2:00 pm
Quiz 3: 4/04/2025 2:00 pm
Quiz 4: 17/04/2025 2:00 pm
Quiz 5: 9/05/2025 2:00 pm
Quiz 6: 23/05/2025 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.
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.
Submission guidelines
Mini-quizzes will be online on Blackboard.
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).
Project 1
- Identity Verified
- In-person
- Mode
- Written
- Category
- Project
- Weight
- 40%
- Due date
This project task needs to be completed during the practical session in Week 7.
- Other conditions
- Time limited.
- 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.
Submission guidelines
The code produced for this project needs to be submitted via Turnitin only at the end of class time.
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
30/05/2025 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 by Turnitin 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 outlined in the course profile, it will be considered a non-submission, resulting in a "fail".
Project 2
- Mode
- Written
- Category
- Project
- Weight
- 40%
- Due date
13/06/2025 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. 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
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% |
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 my.UQ 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
Assessment Submission
It is the responsibility of the student to ensure the on time, correct and complete submission of all assessment items.
Please ensure you receive and save the submission confirmation for all submitted items, you may be asked to produce this as evidence of your submission.
Applications for Extensions to Assessment Due Dates
Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.
Please note:
- Requests for an extension to an assessment due date must be submitted through your my.UQ portal and you must provide documentation of your circumstances, as soon as it becomes evident that an extension is needed. Your application must be submitted on or before the assessment item's due date and time.
- Applications for extension can take time to be processed so you should continue to work on your assessment item while awaiting a decision. We recommend that you submit any completed work by the due date, and this will be marked if your application is not approved. Should your application be approved, then you will be able to resubmit by the agreed revised due date.
- If an extension is approved, you will be notified via your my.UQ portal and the new date and time for submission provided. It is important that you check the revised date as it may differ from the date that you requested.
- If the basis of the application is a medical condition, applications should be accompanied by a medical certificate dated prior to the assignment due date. If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a written statement (Word document) outlining why you cannot provide the documentation. You must then upload the documentation to the portal within 24 hours.
- If an extension is being sought on the basis of exceptional circumstances, it must be accompanied by supporting documentation (eg. Statutory declaration).
- For extensions based on a SAP you may be granted a maximum of 7 days (if no earlier maximum timeframe applies). See the Extension or Deferral availability section of each assessment for timeframes. Your SAP is all that is required as documentation to support your application. However, additional extension requests for the assessment item will require the submission of additional supporting documentation e.g., a medical certificate. All extension requests must be received by the assessment due date and time.
- An extension for an assessment item due within the teaching period in which the course is offered, must not exceed four weeks in total. If you are incapacitated for a period exceeding four weeks of the teaching period, you are advised to apply for Removal of Course.
- If you have been ill or unable to attend class for more than 4 weeks, you are advised to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty - seek advice from the Faculty that administers your program.
- Students may be asked to submit evidence of work completed to date. Lack of adequate progress on your assessment item may result in an extension being denied.
- There are no provisions for exemption from an assessment item within UQ rules. If you are unable to submit an assessment piece then, under special circumstances, you may be granted an exemption, but may be required to submit alternative assessment to ensure all learning outcomes are met.
Applications to defer an exam
In certain circumstances you can apply to take a deferred examination for in-semester and end-of-semester exams. You'll need to demonstrate through supporting documentation how unavoidable circumstances prevented you from sitting your exam. If you can’t, you can apply for a one-off discretionary deferred exam.
Deferred Exam requests are submitted online via mySi-net. Requests received in any other way will not be approved. Additional details associated with deferred examinations, including acceptable and unacceptable reasons may be found at my.UQ.
Please note:
- Applications can be submitted no later than 5 calendar days after the date of the original exam.
- There are no provisions to defer a deferred exam. You need to be available to sit your deferred examination.
- Your deferred examination request(s) must have a status of "submitted" in mySI-net to be assessed.
- All applications for deferred in-semester examinations are assessed by the relevant school. Applications for deferred end-of-semester examinations are assessed by the Academic Services Division.
- You’ll receive an email to your student email account when the status of your application is updated.
- If you have a medical condition, mental health condition or disability and require alternative arrangements for your deferred exam you’ll need to complete the online alternative exam arrangements through my.UQ. This is in addition to your deferred examinations request. You need to submit this request on the same day as your request for a deferred exam or supplementary assessment. Contact Student Services if you need assistance completing your alternative exam arrangements request.
Turnitin
All written assessment must be submitted via the appropriate Turnitin submission portal, which can be found within the Blackboard site. You are responsible for ensuring that your submission is complete. It is wise to re-enter the Turnitin portal and confirm that your submission is there and that it has not been altered during the submission process.
By submitting work through Turnitin you are deemed to have accepted the following declaration “I certify that this assignment is my own work and has not been submitted, either previously or concurrently, in whole or in part, to this University or any other educational institution, for marking or assessment”.
In the case of a Blackboard outage, please contact the Course Coordinator as soon as possible to confirm the outage with ITS.
Assessment/Attendance
Please notify your Course Coordinator as soon as you become aware of any issue that may affect your ability to meet the assessment/attendance requirements of the course. The my.UQ website and the Course Profile for your course also provide information about your course requirements, the rules associated with your courses and services offered by the University.
A note for repeating students in this course
Any student who enrols in a course must not be given exemption or partial credit from their previous attempt(s) for any individual piece of assessment. Instead, the student must successfully complete all of the learning activities and assessment items within the study period of enrolment (PPL Assessment - Procedures).
If the same assessment item is set from one year to the next, repeating students are allowed to submit the same work they submitted in previous attempts at the course. Where possible SENV recommends that you use the feedback you received in your last attempt to improve parts of the item where you lost marks. Resubmission of an altered or unaltered assessment item by a repeating student (where the same assessment has been set) will not be considered as self-plagiarism.
Plagiarism
You should be aware that the University employs purpose built software to detect plagiarism. It is very important that you understand clearly the practical meaning of plagiarism.
DEFINITION OF PLAGIARISM: Plagiarism is the act of misrepresenting as one's own original work the ideas, interpretations, words or creative works of another. These include published and unpublished documents, designs, music, sounds, images, photographs, computer codes and ideas gained through working in a group. These ideas, interpretations, words or works may be found in print and/or electronic media.
EXAMPLES OF PLAGIARISM:
1. Direct copying of paragraphs, sentences, a single sentence or significant parts of a sentence;
2. Direct copying of paragraphs, sentences, a single sentence or significant parts of a sentence with an end reference but without quotation marks around the copied text;
3. Copying ideas, concepts, research results, computer codes, statistical tables, designs, images, sounds or text or any combination of these;
4. Paraphrasing, summarising or simply rearranging another person's words, ideas, etc without changing the basic structure and/or meaning of the text;
5. Offering an idea or interpretation that is not one's own without identifying whose idea or interpretation it is;
6. A 'cut and paste' of statements from multiple sources;
7. Presenting as independent, work done in collaboration with others;
8. Copying or adapting another student's original work into a submitted assessment item.
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 (24 Feb - 02 Mar) |
General contact hours |
Week 1: Introduction to R and Studio Learning outcomes: L01, L03 |
Week 2 (03 Mar - 09 Mar) |
General contact hours |
Week 2: Working with Data Learning outcomes: L01, L02 |
Week 3 (10 Mar - 16 Mar) |
General contact hours |
Week 3: Working with Data (continued after cyclone-break) Learning outcomes: L01 |
Week 4 (17 Mar - 23 Mar) |
General contact hours |
Week 4: Data Wrangling Learning outcomes: L01 |
Week 5 (24 Mar - 30 Mar) |
General contact hours |
Week 5: Special Types of Data Learning outcomes: L01, L02 |
Week 6 (31 Mar - 06 Apr) |
General contact hours |
Week 6: Data Visualisation Learning outcomes: L01, L02 |
Week 7 (07 Apr - 13 Apr) |
General contact hours |
Week 7: Visualising Spatial Data Learning outcomes: L03 |
Week 8 (14 Apr - 20 Apr) |
General contact hours |
Week 8: Simulating Data Learning outcomes: L01, L04 |
Week 9 (28 Apr - 04 May) |
General contact hours |
Week 9: Simulating Time Series Learning outcomes: L01, L04 |
Week 10 (05 May - 11 May) |
General contact hours |
Week 10: Statistical Modelling and Learning Learning outcomes: L04 |
Week 11 (12 May - 18 May) |
General contact hours |
Week 11: Principles of Machine Learning Learning outcomes: L04 |
Week 12 (19 May - 25 May) |
General contact hours |
Week 12: Supervised Machine Learning Learning outcomes: L04 |
Week 13 (26 May - 01 Jun) |
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 - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.