Skip to menu Skip to content Skip to footer
Course profile

Data Analytics in Civil Engineering (CIVL3530)

Study period
Sem 1 2025
Location
St Lucia
Attendance mode
In Person

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

Guest lecturer

Dr Weiming Zhao
Dr Julien Monteil

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
  • In-person
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
  • Hurdle
  • Identity Verified
  • In-person
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
Clear filters
Learning period Activity type Topic
Multiple weeks

From Week 1 To Week 13
(24 Feb - 01 Jun)

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:

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: