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
- Civil Engineering School
Understanding data, identifying patterns and knowledge from data, developing models to use data to estimate or predict phenomena are essential skills to civil engineers in the 21st century. Civil engineers collect and analyse large amounts of data almost on a daily basis to make better and more informed decisions. Mastering common data analytic techniques therefore essential for a productive civil engineering career. This course is designed to meet this critical need by providing students with the necessary skills and knowledge in processing, analysing, and interpreting data collected in civil engineering. This includes techniques for handling common data issues, approaches for exploratory data analysis, and methods for confirmatory data analysis. A range of popular techniques are covered, including descriptive analysis, imputation and denoising, comparison analysis, pattern classification and recognition, estimation and prediction, etc. Each technique's application in civil engineering is the focus of the course.
The ability of understanding study types, variable types and data collection, properly processing data, correctly describing data, extracting useful information from data, and making data-based decisions is becoming increasingly important in many fields, and Civil Engineering is no exception as civil engineers regularly and frequently need to collect and analyse a huge amount of data. Thus, mastering common data analytics techniques is imperative for students in civil engineering to survive and excel in their career. This data-focused compulsory course in the Civil Engineering Specialisation is designed to meet this critical need by providing students with the necessary skills and knowledge in data collection, processing, analysing, and interpreting data collected in civil engineering. This includes techniques for handling common data issues, approaches for exploratory data analysis, and methods for confirmatory data analysis. A range of popular techniques are covered, including descriptive analysis, imputation and denoising, regression analysis for different variable types, pattern classification and recognition, estimation and prediction, etc. Each technique's application in civil engineering is the focus of the course.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
MATH1051 and MATH1052 and ENGG1001 and CIVL2530.
Course contact
Course staff
Lecturer
Professor Zuduo Zheng
Guest lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
This course aims to provide students with the necessary skills and knowledge in processing, analysing, and interpreting data collected in civil engineering. This includes techniques for handling common data issues, approaches for exploratory data analysis, and methods for confirmatory data analysis. A range of popular techniques are covered, including descriptive analysis, imputation and denoising, comparison analysis, regression analysis, pattern classification and recognition, estimation and prediction, etc.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Identify and explain common data issues including missing values, data noise and outliers.
LO2.
Apply common data imputation and denoising techniques.
LO3.
Describe data using descriptive statistics and identify patterns in data.
LO4.
Apply data analysis techniques to compare data using appropriate statistical tests.
LO5.
Apply data analysis methods to explore and model relationships between two or more variables of interest.
LO6.
Select and implement appropriate data-driven methods/models to reveal patterns & insights and make predictions.
LO7.
Effectively interpret the results and communicate outcomes to the general public as well as experts in the field using a variety of professional communication styles.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code | Data processing and modelling | 25% |
16/04/2025 4:00 pm |
| Computer Code | Computer Exercise on Data-driven Methods | 25% |
26/05/2025 4:00 pm |
| Participation/ Student contribution, Quiz |
In-class quizzes
|
10% |
Marks: 1.66 26/02/2025 4:00 pm Marks: 1.66 5/03/2025 4:00 pm Marks: 1.66 12/03/2025 4:00 pm Marks: 1.66 19/03/2025 4:00 pm Marks: 1.66 26/03/2025 4:00 pm Marks: 1.66 2/04/2025 4:00 pm Marks: 1.66 9/04/2025 4:00 pm Marks: 1.66 16/04/2025 4:00 pm Marks: 1.66 30/04/2025 4:00 pm Marks: 1.66 7/05/2025 4:00 pm
Throughout the semester |
| Examination |
Final Exam
|
40% |
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
Data processing and modelling
- Mode
- Written
- Category
- Computer Code
- Weight
- 25%
- Due date
16/04/2025 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
This assessment will require you to process a data set using the techniques introduced in the lectures and then develop a linear regression model using the processed data.
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
Assignment must be submitted via Turnitin 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
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.
Computer Exercise on Data-driven Methods
- Mode
- Written
- Category
- Computer Code
- Weight
- 25%
- Due date
26/05/2025 4:00 pm
- Learning outcomes
- L03, L04, L05, L06, L07
Task description
This assessment will require you to explore patterns in a data set using the data-driven methods introduced in the lectures.
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
Assignment must be submitted via Turnitin 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
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.
In-class quizzes
- In-person
- Mode
- Written
- Category
- Participation/ Student contribution, Quiz
- Weight
- 10%
- Due date
Marks: 1.66 26/02/2025 4:00 pm
Marks: 1.66 5/03/2025 4:00 pm
Marks: 1.66 12/03/2025 4:00 pm
Marks: 1.66 19/03/2025 4:00 pm
Marks: 1.66 26/03/2025 4:00 pm
Marks: 1.66 2/04/2025 4:00 pm
Marks: 1.66 9/04/2025 4:00 pm
Marks: 1.66 16/04/2025 4:00 pm
Marks: 1.66 30/04/2025 4:00 pm
Marks: 1.66 7/05/2025 4:00 pm
Throughout the semester
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
You are expected to actively participate in lectures throughout the semester. In-class quizzes will contribute to your final marks, with points accruing from Week 1 through to Week 13. The full marks for each quiz are 1.66, and your final score will be calculated based on your best 6 out of 10 quizzes. To ensure your quiz is counted, it must be submitted via Blackboard before the end of each lecture.
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
Submit it to the Blackboard by the end of each lecture.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
All the topics and learning objectives of this course will be covered in the final exam. This exam will consist of MCQ, short answer, and problem solving. Some of the MCQ problems may require some calculations. Some short answer questions will require calculations.
Hurdle requirements
In order to pass the course, students need to obtain at least 40% of the full marks of the 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 - specified written materials permitted |
| Materials | One A4 sheet of handwritten notes, double sided, is 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 | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. Course grade description: Fail :The student fails to provide any evidence of achieving the course learning outcomes. |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. Course grade description: Fail: The student fails to demonstrate any relevant knowledge or understanding of the key concepts. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Fail: Falls short of satisfying all basic requirements for a Pass. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: The student demonstrates a sound knowledge of the underlying concepts of data analytics. The student communicates with some accuracy and relevance to specific information on the subject. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: The student demonstrates a proficient knowledge of the relevant information and a good understanding of the key concepts. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Key concepts are understood. There is a demonstrated ability to solve previously unseen problems. There are only minor factual inaccuracies and there is little irrelevant information. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Key concepts are understood and can be used to solve previously unseen problems. The student's knowledge of the course is comprehensive. There is evidence of critical analysis and an ability to synthesise information from different aspects of the course. There are insignificant factual inaccuracies and there is very limited irrelevant information. |
Additional course grading information
To receive an overall grade of 4 or more, students must complete the final examination with a minimum grade of 3 (45% or more).
Grade cutoffs and hurdles
Final marks will be rounded up to an integer value prior to applying hurdles or grade boundaries.
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.
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 |
Lecture |
Lectures Lectures in correspondence with course aims Learning outcomes: L01, L02, L03, L04, L05, L06, L07 |
Tutorial |
Computer exercise & tutorial Tutorials and computer coding activities. Learning outcomes: L01, L02, L03, L04, L05, L06, 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.
School guidelines
Your school has additional guidelines you'll need to follow for this course: