Course coordinator
Professor Kate O'Brien
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
- Chemical Engineering School
Project based investigation of engineering operations. Effective quantitative sampling & statistical analysis including hypothesis testing, standard probability models, estimation, regression & experimental design. Technical reporting & project management skills
Engineering is at its heart about decision making, usually in the face of incomplete data and uncertainty. Data analysis, combined with observing, measuring, and correcting for uncertainty, are thus an essential part of the education of any engineering student, along with integration of these skills within a student's general engineering practice. In this course, statistical analysis and experimentation are integrated into engineering problem-solving, with a focus on quantitative estimation. That includes using observation of uncertainty to infer its impact on process outputs and parameters. The course incorporates observation of uncertainty, estimation of parameters related to both uncertainty and core processes, and propagation of uncertainty through a process to inform decision-making. The course integrates a problem solving workshop with practical computing sessions to embed data analysis, along with laboratory experimental work to generate real-life data.
Course requirements
Assumed background
1. Basic principles of algebra, including non-linear equations, and analytical integration techniques (area under curve).
2. Principles of mass balancing and mass and energy conservation.
3. Basic knowledge of Excel and python.ﾠﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ENGG1001 and MATH1051. CHEE2001 to be completed prior to or concurrently with this course.
Incompatible
You can't enrol in this course if you've already completed the following:
STAT1201 and STAT1301 and STAT2203
Course contact
Course staff
Lecturer
Professor Kate O'Brien
Dr Hong Peng
Dr Jason Wen
Tutor
Ms Alia Gallet--Pandelle
Mr Ronny Pibaque
Miss Natalie Teng
Mr Lee Burns
Miss Rachel Fitzgerald
Miss Rebecca Hall
Ms Rachel Mugumo
Mr Mohamed Ibrahim
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Each week, there are the following learning activities:
- a two hour workshop, where students complete activities designed for deep learning of core concepts
- a two hour tutorial, where students apply those core concepts in practical examples using Python and Excel software
- short videos, which are designed to be viewed either before, between or after the workshop and tutorials to consolidate learning
- a weekly online quiz, to reinforce key concepts (students will receive a mark for attempting this quiz, regardless of their grade, to encourage students to test their learning without fear of "getting it wrong")
Students will also complete a practical experiment in week 7, and the data from this experiment will be used in the second and third submissions for the coffee project.
Aims and outcomes
This course aims to teach you the basic principles of engineering investigation and data analysis. More specifically, you should be able to:
- Apply basic statistical methods ﾠin data collection and analysis, and communicate the implications of this analysis.
- Communicate uncertainty in observations and derived properties.
- Describe the implications of uncertainty for process engineering.
- Design experiments and analyse data with appropriate consideration of ﾠthe limitations associated with sample size.
- Conduct experiments to explore relationships between process inputs and outputs and quantify the associated uncertainties
- Estimate parameters that represent relationships between variables.
- Predict the impact of input variability on process outputs.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Data Analysis: Collect, import, tidy, clean, summarize and visualize data to characterise key elements of the system from which the data was taken, including central tendency, variability and distribution of data. Conduct and report on experiments safely and effectively. Apply appropriate statistical methods to data from an engineering application to answer relevant questions and inform decisions. Outline ethical principles of data analysis, including data sovereignty.
LO2.
Use of appropriate tools: Undertake data analysis and modelling using EXCEL spreadsheets and coding in Python, supported by use of Artificial Intelligence (AI) tools.
LO3.
Statistical modelling: Develop statistical models based on data, using linear and non-linear regression. Determine the significance, uncertainty and physical meaning of model parameters. Undertake basic model calibration and validation, and communicate uncertainty in model predictions.
LO4.
Uncertainty analysis: Explain how multiple sources influence observed uncertainty. Identify and calculate uncertainty in simple models based on uncertainty in model inputs. Apply numerical propagation to quantify uncertainty for more complex processes.
LO5.
Communication: Use critical thinking to communicate outcomes of data analysis clearly and concisely, using both text and figures, in a manner appropriate to the target audience.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Practical/ Demonstration |
Online Lab OH&S training
|
Compulsory |
7/04/2025 2:00 pm
Students must complete the online OH&S training in order to participate in the practical, where data will be collected for use in the second and third stage submissions for the coffee project. |
| Quiz |
Weekly online quiz
|
5% |
3/03/2025 2:00 pm 17/03/2025 2:00 pm 24/03/2025 2:00 pm 31/03/2025 2:00 pm 7/04/2025 2:00 pm 14/04/2025 2:00 pm 28/04/2025 2:00 pm 6/05/2025 2:00 pm 12/05/2025 2:00 pm 19/05/2025 2:00 pm 26/05/2025 2:00 pm
Only 7 quizzes of 11 must be completed to receive the 5 % grade, therefore there will be no extensions granted. |
| Project |
Coffee project
|
45% (Part 1 10 %; Part 2 15 %; Part 3 20%) |
Project Part 1 - Analyse 24/03/2025 2:00 pm Project Part 2 - Measure 2/05/2025 2:00 pm Project Part 3 - Model 30/05/2025 2:00 pm |
| Examination |
Exam During Exam Period (Central)
|
50% Individual - hurdle |
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 Lab OH&S training
- Online
- Mode
- Activity/ Performance
- Category
- Practical/ Demonstration
- Weight
- Compulsory
- Due date
7/04/2025 2:00 pm
Students must complete the online OH&S training in order to participate in the practical, where data will be collected for use in the second and third stage submissions for the coffee project.
Submission guidelines
Online Submission.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Weekly online quiz
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 5%
- Due date
3/03/2025 2:00 pm
17/03/2025 2:00 pm
24/03/2025 2:00 pm
31/03/2025 2:00 pm
7/04/2025 2:00 pm
14/04/2025 2:00 pm
28/04/2025 2:00 pm
6/05/2025 2:00 pm
12/05/2025 2:00 pm
19/05/2025 2:00 pm
26/05/2025 2:00 pm
Only 7 quizzes of 11 must be completed to receive the 5 % grade, therefore there will be no extensions granted.
Task description
Weekly online quizzes designed to reinforce students' understanding of course content, and provide the opportunity to test their knowledge. Marks are allocated on the basis of number of questions attempted, regardless of the answers - this is done to encourage students to test themselves without worrying about the outcome in order to enhance their learning. The quizzes will take approximately 10 minutes to complete and will be available for a week prior to due date. 0.714 % of overall grade will be awarded for each quiz completed, regardless of the score achieved. Students can retake the quizzes at any time.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Only 7 quizzes of 11 must be completed to receive the 5 % grade, therefore there will be no extensions granted.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Quizzes will be available for a week prior to due date, and students can miss up to four quizzes and still attain the full 5 %.
Coffee project
- Online
- Mode
- Written
- Category
- Project
- Weight
- 45% (Part 1 10 %; Part 2 15 %; Part 3 20%)
- Due date
Project Part 1 - Analyse 24/03/2025 2:00 pm
Project Part 2 - Measure 2/05/2025 2:00 pm
Project Part 3 - Model 30/05/2025 2:00 pm
Task description
Students will analyse, measure and model data related to coffee production in a three part project. Students will use data analysis, experimental results and modelling to explore different aspects of the current coffee-making methodology, identify current issues and recommend improvements. Uncertainty will be quantified, and the implications of uncertainty for decision-making discussed. Results need to be communicated in a clear and concise form, demonstrating critical thinking and using appropriate figures. In Parts 2 and 3 of the project, students will use data collected in the practical experiment undertaken 8-11 April. Data will be collected in teams, but submission will be individual. A portion of the mark for Part 2 will be allocated to data collection in the practical experiment.
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
Online submission via Blackboard. Task sheet and marking criteria will be provided 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.
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.
Exam During Exam Period (Central)
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50% Individual - hurdle
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
Task description
Closed book examination of 2 hours duration, with no access to generative Artificial Intelligence (AI) or Machine Translation (MT) tools. A UQ approved calculator will be required for this exam. The exam is a course hurdle - you must achieve at least 50 % on the exam to pass the course.
Hurdle requirements
You must pass the exam to pass the course, and final grades are capped by mark in final exam.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| 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 | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: Typically less than 20% overall. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: Typically between 20 and 44% overall. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Typically between 45 and 49% overall OR overall mark greater than 50% but less than 50 % on final exam. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Typically between 50 and 64% overall, with at least 50% in the final exam. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Typically between 65 and 74% overall, with at least 60% in the final exam. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Typically between 75 and 84% overall, with at least 70% on the final exam. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Typically 85% and above overall, with at least 80% on the final exam. |
Supplementary assessment
Supplementary assessment is available for this course.
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
See additionalﾠ exercises available on blackboard.
ﾠ
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 From Week 1 To Week 13 |
Workshop |
Weekly workshop In weekly workshops, students will undertake problem-solving activities to practice application of key concepts taught in the course. These activities are designed to build students' knowledge and skills, and prepare students for both the project assessment and final exam. |
Workshop |
Weekly tutorial In the tutorials, student will apply key concepts to engineering problem-solving using Python and EXCEL software. Students should bring their own laptop to the tutorials, loaded with appropriate software, as per instructions on Blackboard. The activities in the tutorials are designed to build the skills and knowledge required by students to successfully complete the coffee project, and to complement the activities in the workshop, the video content and the weekly online quizzes in building the skills and knowledge required to pass the final exam. |
|
Not Timetabled |
Weekly videos Each week, content to support the workshop and tutorials will be made available in short videos. The workshops, tutorials, videos and weekly quizzes are designed to complement each other and ensure that students have both underpinning knowledge and skills, and practice in applying that knowledge and skills in engineering problem-solving. The videos can be viewed prior to, between or following the weekly workshops and tutorials, but should be viewed each week to ensure that students develop deep conceptual learning of the course content. |
|
Week 7 (07 Apr - 13 Apr) |
Practical |
Practical All students will complete a two hour practical laboratory experiment in week 7, to measure data required to complete the second and third stages of the coffee project. In addition to the online OH&S training, students will also need to complete an induction to the undergraduate student laboratories in Building 46, the Andrew Liveris Building, if they have not already done so. This will be done as part of the prac. Students should be familiar with University policy Laboratory Safety in Teaching Laboratories (https://ppl.app.uq.edu.au/content/2.30.14-occupational-health-and-safety-laboratory). Pertinent information is in the mandatory online module and assessment, UGRD01. UGRD01 can be found on Blackboard in the Training courses Tab > UQ Workplace Inductions and OHS Training > UG Lab Students. UGRD01 only needs to be completed once. The Minimum PPE required across all School of Chemical Engineering undergraduate laboratories is: safety spectacles or over glasses, lab coat, long trousers that cover the ankles and fully enclosed shoes. Laboratory and practical specific information is given at beginning of each practical. It is important you arrive on time or you may not be allowed into the laboratory. Exceptions to the above will only be made under the same circumstances for any other exam (e.g. in cases of genuine illness where a medical certificate is provided). In such circumstances a single “special” safety induction will be held. For further information, contact the Course Coordinator. |
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.
School guidelines
Your school has additional guidelines you'll need to follow for this course:
- Safety Induction for Practicals
Course guidelines
Anyone undertaking courses with a practical component must complete the UQ Undergraduate Student Laboratory Safety Induction and pass the associated assessment.
Specific instructions, usage guidelines and rules for each of the undergraduate laboratories will be delivered as part of each course.
In some cases, students may be required to attend a specific face-to-face laboratory induction/training session.