Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 4
- Administrative campus
- St Lucia
- Coordinating unit
- The Environment School
Students will conduct research and analyses based on pre-existing data sets from a laboratory at UQ or a partner institute or organisation.
Students need to achieve a GPA 5.5 over the first 12 units of QBIO courses to receive permission to enrol in this course.
This courses is restricted to students enrolled in the Master of Quantitative Biology programs.
Please apply for permission to enrol in this course using the ONLINE FORM - https://survey.app.uq.edu.au/7f1d6b148b62403a98cf05200ab51622
QBIO7009 is an optional final course in the Quantitative Biology Masters program in the School of the Environment. The vision is for high performing students to be offered an opportunity for work-integrated learning (WIL) by conducting a programming or software development project with an employer for approximately 240 h (4 units). The course is listed in the course catalogue as follows:
QBIO students will apply their recently gained knowledge and skills to real data sets provided by academics at The University of Queensland, and employers at research institutes such as CSIRO, DAF, and QAAFI. Students will execute required and sophisticated analyses of a large data set. Students will report on their programming code, and produce useful outcomes for the laboratory, and the scientific community. This course will ask students to demonstrate their expertise in executing “big data” analyses in biology, while at the same time evaluating their ability to communicate the significance of their contributions to a general field of research in biology such as ecology, evolution, systems biology, conservation biology, breeding, human population genetics, etc.
Course requirements
Assumed background
Students should have read and understood the course manual
Prerequisites
You'll need to complete the following courses before enrolling in this one:
QBIO7001, and at least three courses from QBIO7002, QBIO7003, QBIO7004, QBIO7005 & QBIO7006
Restrictions
The course is restricted to students enrolled in the Master of Quantitative Biology program. Students need to achieve a GPA 5.5 over the first 12 units of QBIO courses to receive permission to enrol.
Jointly taught details
This course is jointly-taught with:
- Another instance of the same course
external version of same course
Course contact
Course staff
Lecturer
Timetable
Additional timetable information
Under the original Masters program design by the QBIO Masters program developers, QBIO7009 will run in the final 6 weeks of semester 2, after completion of QBIO7008, as a 6 in QBIO7008 is a pre-requisite. This will make time extremely tight, so enrolling students need to be focussed and have engaged an industry placement in advance of the course commencement. Students are encouraged to read the full course manual before the start of semester so that they are very clear about the goals for the course and the assessment.
Aims and outcomes
This course aims to provide students with a work-integrated learning experience based on a research intensive project with an industry partner. Assessment aims to encourage students to develop non-technical competencies in workplace organisational behaviour while practising their technical skills in data analysis within the workplace.
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Learning outcomes
After successfully completing this course you should be able to:
LO1.
Use modern computing approaches to solve common goals
LO2.
Communicate complex analyses orally and in writing for a variety of audiences, including scientists, industry representatives, policy makers and the general public
LO3.
Analytically integrate ecological and genetic data
LO4.
Write reports that integrate text, code, data, analyses, and graphics using R Markdown
LO5.
Visualise complex data in creative ways
LO6.
Produce well-documented R packages and interactive Shiny apps
LO7.
Learn how to efficiently work in teams to solve coding problems arising in the biological sciences
LO8.
Conduct reflective self-assessment on achievements and professional development
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project | Computational Project plan | Pass/Fail |
25/09/2024 2:00 pm |
| Project | Project Final Report | 70% |
1/11/2024 2:00 pm |
| Presentation | Project Final Seminar | 30% |
6/11/2024 2:00 pm |
| Performance | Project supervisor's report | Pass/Fail |
8/11/2024 2:00 pm |
Assessment details
Computational Project plan
- Mode
- Written
- Category
- Project
- Weight
- Pass/Fail
- Due date
25/09/2024 2:00 pm
- Learning outcomes
- L02, L03, L07
Task description
Write a project plan documenting all of the deliverables and subdeliverables of the project. Use a heirarchical structure that fully captures all of the work required to deliver the final output (eg. code, dashboard, tool)
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
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.
Project Final Report
- Mode
- Written
- Category
- Project
- Weight
- 70%
- Due date
1/11/2024 2:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07, L08
Task description
Produce a comprehensive final report fully describing the output delivered. The format will depend upon the nature of the placement and may be, for example, a standard report, or may be a Github page. Agreement between the student, the placement supervisor and the coordinator must be obtained and documented in the Project Plan (assessment 1). All source code must be available for assessment in the project final report, regardless of format. A self-reflective piece of about 2 pages at the end of the report is a requirement for this assessment.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Project Final Seminar
- Mode
- Activity/ Performance
- Category
- Presentation
- Weight
- 30%
- Due date
6/11/2024 2:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Present the project outcomes in a clear, engaging way, that is accessible to a non-coding audience
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Project supervisor's report
- Mode
- Activity/ Performance
- Category
- Performance
- Weight
- Pass/Fail
- Due date
8/11/2024 2:00 pm
- Learning outcomes
- L07, L08
Task description
A questionnairre will be sent to the project supervisor to record performance and satisfaction.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
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: <p>1-24.4%</p> |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: <p>24.5-44.4%</p> |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: <p>44.5-49.4%</p> |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: <p>49.5-64.4%</p> |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: <p>64.5-74.4%</p> |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: <p>74.5-84.4%</p> |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: <p>84.5%+</p> |
Additional course grading information
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Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Applications for Extensions
Information on applying for an extension can be found here - my.UQ Applying for an extension
Extension applications must be received by the assessment due date and time.
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 Word document outlining why you cannot provide the documentation and upload the documentation to the portal within 24 hours.
Please note: While your extension request is being considered, you should work towards completing and submitting your assessment as soon as possible.
If you have been ill or unable to attend class for more than 4 weeks in a semester, we advise you to carefully consider whether you are capable of successfully completing your courses. You might need to consider applying for removal of course. We strongly recommend you seek advice from the Faculty that administers your program.
Extensions with Student Access Plans (SAP)
For extensions up to 7 days, your SAP is all that is required as documentation to support your application. However, extension requests longer than 7 days (for any one assessment item) will require the submission of additional supporting documentation e.g., a medical certificate. A maximum of two applications may be submitted for any one assessment item, unless exceptional circumstances can be demonstrated. All extension requests must be received by the assessment due date and time.
ARTIFICIAL INTELLIGENCE USE (AI)
The assessment tasks in this course evaluate students’ abilities, skills, and knowledge without the aid of Artificial Intelligence (AI).
Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute misconduct under the Student Code of Conduct.
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.
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 |
|---|---|---|
Multiple weeks |
Placement |
Placement To be agreed between student, assessor, coordinator and placement supervisor Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08 |
No student involvement (Breaks, information) |
<font color="red">EKKA Day |
|
Placement |
Pre-placement Interview Meeting with placement supervisor, course coordinator, assessor and student. To be scheduled by the student. Learning outcomes: L02 |
|
No student involvement (Breaks, information) |
<font color ="Red">King's Birthday |
|
Placement |
Post-placement interview Placement/project debrief with assessor, coordinator and placement supervisor Learning outcomes: L08 |
|
Seminar |
Project Final Seminar Present computational project output to a general audience Learning outcomes: L02, L03, L05, L07 |
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.