Course coordinator
Associate Professor Michael Bulmer
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Mathematics & Physics School
This course introduces computational statistics with applications to bioinformatics. It provides an accelerated introduction to statistics and statistical programming in R. The course then builds on these by using R to explore applications of statistics in science and molecular biology in particular.
Statistics is the science of learning from data. It involves designing studies or experiments, collecting data and modelling or analyzing data for the purpose of scientific discovery and decision-making. In biological sciences, statistics is an essential tool to unravel information contained in large data sets.
This course introduces essential concepts of statistics and explains how to make sense of data by applying commonly used techniques for the analysis of gene expression data and biological data, including common statistical tests, linear regression analysis, clustering methods, normalization and data mining. Practical work focuses on learning R, the popular scripting language for data manipulation and analysis, and Bioconductor.
Course requirements
Companion or co-requisite courses
You'll need to complete the following courses at the same time:
BINF6000 or equivalent
Restrictions
STAT7174 restricted to students in Master of Bioinformatics and Master Engineering Science (Bioengineering). Other students require permission of Head of School.
Course contact
Course staff
Lecturer
Associate Professor Michael Bulmer
Professor Jessica Mar
Tutor
Miss . Namuhan
Ms Maia Thomas
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures and practicals will be held in person for all students. Lectures and practicals will start in Week 1.
Note that there is no lecture on Monday, ﾠMay 5th (due to the public holiday).
Aims and outcomes
The aim of this course is to provide students with the skills necessary to process and analyse large biological data sets using the R statistical programming language. This course will enhance your employability skills such as analysing and extracting useful information from data, the ability to think critically and assess the given information, and communicating the insights with peers and stakeholders.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Use statistical methodology to draw conclusions in a range of applied settings
LO2.
Demonstrate proficiency in statistical programming
LO3.
Use computational methods to deal with large data sets
LO4.
Effectively and appropriately communicate insights from data
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
Online Quizzes
|
10% |
21/03/2025 3:00 pm 4/04/2025 3:00 pm 17/04/2025 3:00 pm 9/05/2025 3:00 pm 23/05/2025 3:00 pm |
| Computer Code, Project | Assignment 1 | 20% |
11/04/2025 3:00 pm |
| Computer Code, Presentation, Project |
Assignment 2
|
40% |
28/05/2025 3:00 pm |
| Examination |
Practical Exam
|
30% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
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
Online Quizzes
- Online
- Mode
- Product/ Artefact/ Multimedia
- Category
- Quiz
- Weight
- 10%
- Due date
21/03/2025 3:00 pm
4/04/2025 3:00 pm
17/04/2025 3:00 pm
9/05/2025 3:00 pm
23/05/2025 3:00 pm
- Learning outcomes
- L02
Task description
During semester there will be five online quizzes to test your skills in using R. Each of the quizzes is worth 2% of your overall grade.
You can attempt the online quiz questions as many times as you like while the quiz is open. Your mark for each quiz will be the maximum mark you obtain from your attempts by the time the quiz close.
Quiz 1 will open at 15:00 on Friday, February 28th, with the remaining quizzes opening when the preceding quiz closes.
You are encouraged to discuss the quiz questions with other students using the Ed Discussion Board on Blackboard.
Submission guidelines
Complete the quiz through 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.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Assignment 1
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Project
- Weight
- 20%
- Due date
11/04/2025 3:00 pm
- Learning outcomes
- L01, L02, L04
Task description
Assignment 1 will assess R skills and the interpretation of statistical tests.
This assignment will be broken into different exercises covering the content of Weeks 1 - 5.
Submission guidelines
Submit through 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 ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.
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.
Assignment 2
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Presentation, Project
- Weight
- 40%
- Due date
28/05/2025 3:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
Assignment 2 will assess the statistical analysis of a biological data set.
This assignment will be broken into different exercises, following the sequence of topics from the course.
You will create a technical report of your analysis, along with a recorded video presentation summarising your results and insights.
Assignment 2 can be completed individually or in a group of two students. If working in a group, you will submit a single group submission and both members of the group will receive the same grade for the assignment.
Submission guidelines
Submit your report and video presentation via 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 ADDITIONAL ASSESSMENT INFORMATION for further extension/deferral information relating to this assessment item.
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.
Practical Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Examination
- Weight
- 30%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Learning outcomes
- L01, L02, L04
Task description
The Practical Exam will cover the skills and understandings developed through the course.
Hurdle requirements
See ADDITIONAL COURSE GRADING INFORMATION for information relating to this hurdle requirement.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open Book examination |
| Exam platform | Other |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. Course grade description: You will be able to partially apply statistical methodologies and analyse very few important practical settings. Written reports will be poor and the understanding of the statistical theories and applications may be missing. |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. Course grade description: You will demonstrate some knowledge of the basic concepts in the course. You will be able to partially apply statistical methodologies and analyse a few important practical settings. Written reports may be poor and the understanding of the statistical theories and applications may be weak. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: You will demonstrate some knowledge of the basic concepts in the course. You will be able to apply statistical methodologies and analyse important practical settings. You will be able to describe and apply some statistical theories and methods in the course, and be able to communicate your methods and results. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: You will demonstrate an understanding of the basic concepts in the course. You will be able to apply statistical methodologies and analyse important practical settings, identifying some key assumptions and parameters that might affect the modelling. You will be able to describe and apply many statistical theories and methods in the course, and be able to communicate your methods and results. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: You will demonstrate an adequate understanding of the course material. You will be able to apply statistical methodologies and analyse many practical settings, identifying the key assumptions and parameters that might affect the modelling. You will be able to describe and apply most statistical theories and methods in the course, and be able to communicate your methods and results both verbally and in writing. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: You will demonstrate a comprehensive understanding of the course material. You will be able to apply statistical methodologies and analyse most practical settings, identifying important assumptions and parameters and other factors that might affect the modelling. You will show confidence in describing and applying statistical theory and methods, and proficiency in communicating your methods and results both verbally and in writing. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: You will demonstrate an excellent understanding of the course material. You will be able to model and analyse a broad range of practical settings, providing insight and thoroughness in the form of assumptions and parameters and other factors that might affect the modelling. You will show a high-level of confidence in describing and applying mathematical theory and methods, and excellent proficiency in communicating your methods and results both verbally and in writing. |
Additional course grading information
If you receive less than 40% of the available marks on the Practical Exam then the maximum grade you can obtain is 3.
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 in this course will be a 2 hour examination similar in style to the Practical Exam. To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.
Additional assessment information
Artificial Intelligence
Assessment tasks in this course evaluate students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the se of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
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 date applies). See the Extension or Deferral availability section of each assessment for details. 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.
- 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.
- If you have been ill or unable to attend class for more than 14 days, 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.
- 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.
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
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
You will need to have your own laptop with R and RStudio installed to bring to the weekly practicals and the Practical Exam, as well as for working on the online quizzes and assignments. Tutors can help you install R and RStudio in the Week 1 practical class.
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) |
Lecture |
Exploratory data analysis Learning outcomes: L01, L04 |
Practical |
Getting started with R Learning outcomes: L02, L03 |
|
Week 2 (03 Mar - 09 Mar) |
Lecture |
Hypothesis testing and inference Learning outcomes: L01, L04 |
Week 3 (10 Mar - 16 Mar) |
Practical |
Data structures in R Learning outcomes: L02, L03 |
Week 4 (17 Mar - 23 Mar) |
Lecture |
Comparing independent groups Learning outcomes: L01, L04 |
Practical |
Visualisation Learning outcomes: L02, L03, L04 |
|
Week 5 (24 Mar - 30 Mar) |
Practical |
Hypothesis testing Learning outcomes: L02, L03, L04 |
Lecture |
Simple linear regression Learning outcomes: L01, L04 |
|
Week 6 (31 Mar - 06 Apr) |
Practical |
Regression and correlation Learning outcomes: L02, L03, L04 |
Lecture |
Multiple and generalised linear regression Learning outcomes: L01, L04 |
|
Week 7 (07 Apr - 13 Apr) |
Practical |
Multiple and generalised linear regression Learning outcomes: L02, L03, L04 |
Lecture |
RNA-seq Learning outcomes: L01, L04 |
|
Week 8 (14 Apr - 20 Apr) |
Practical |
Bioconductor and RNA-seq Learning outcomes: L02, L03 |
Lecture |
RNA-seq Learning outcomes: L01, L04 |
|
Week 9 (28 Apr - 04 May) |
Practical |
Bioconductor and RNA-seq Learning outcomes: L02, L03 |
Lecture |
Classification Learning outcomes: L01, L04 |
|
Week 10 (05 May - 11 May) |
Practical |
Classification Learning outcomes: L02, L03, L04 |
Week 11 (12 May - 18 May) |
Lecture |
Clustering Learning outcomes: L01, L04 |
Practical |
Clustering Learning outcomes: L02, L03, L04 |
|
Week 12 (19 May - 25 May) |
Lecture |
Experimental design Learning outcomes: L01, L04 |
Practical |
Assignment Support Learning outcomes: L02, L03, L04 |
|
Week 13 (26 May - 01 Jun) |
Lecture |
Review Learning outcomes: L01, L04 |
Practical |
Mock practical exam Learning outcomes: L02, L03 |
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.