Engineering Data Analytics and Applications
Overview
This unit aims to help students develop their conceptual and practical understanding of data analytics, numerical algorithms and computational methodologies in the context of Civil Engineering applications. Students will learn the basic concepts, techniques and industry-standard tools that characterise professional data analytics. They will also work in groups on a project to acquire relevant fundamental skills to help them think analytically using data modelling and visualisation to support evidence-based decision-making. The unit will primarily present Civil Engineering problems in a computational setting with emphasis on data science and advanced computational and artificial intelligence techniques. Upon completing this unit, students will have a strong understanding of Python programming and its varied applications for data analytics in Civil Engineering; and of MATLAB in the context of statistical analysis, data analytics, interactive and highly customisable data visualisations, artificial intelligence and machine learning applications.
Requisites
02-November-2025
Learning outcomes
Students who successfully complete this unit will be able to:
- Explain the basic principles of data science, analytics and numerical algorithms and use them in existing and emerging Civil Engineering applications. [K1, K2, K3, K4]
- Apply problem-solving methodologies to generate, evaluate and justify innovative solutions. [S1, S2]
- Demonstrate effective professional written and oral communication through reporting, analyses, documentation and presentations. [A2, A3, A4]
- Develop structured scripting codes using Python to perform complex tasks in data analytics, improve data visualisation and uncover complex patterns and insights. [K2, K4, S1, S2, S3]
- Demonstrate effective use of MATLAB to apply machine learning and artificial intelligence solutions to complex smart infrastructure applications. [K2, K3, K4, S1, S2, S3]
- Develop effective reports supplemented with quality visualisations and analyses to support evidence-based decision making. [K2, K3, S2, S4]
- Demonstrate effective team membership and leadership as part of a diverse engineering team, and use engineering methods in project management and appraisal. [A5, A6, A7]
Teaching methods
Hawthorn
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| Face to Face Contact (Phasing out) Lecture | 1.00 | 12 weeks | 12 |
| Face to Face Contact (Phasing out) Computer Laboratory | 2.00 | 12 weeks | 24 |
| Unspecified Learning Activities (Phasing out) Independent Learning | 1.00 | 12 weeks | 12 |
| Unspecified Learning Activities (Phasing out) Individual Study | 8.50 | 12 weeks | 102 |
| TOTAL | 150 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Assignment | Group | 35 - 45% | 1,2,4,5,7 |
| Presentation | Group | 15 - 25% | 3,7 |
| Project Report | Group | 35 - 45% | 1,2,3,6,7 |
Content
- Data analytics and machine learning techniquesÂ
- Smart buildings and energy management systems; asset management and predictive maintenance; environmental, hydrological and water resources modelling; geotechnical engineering and infrastructural lifelines; construction engineering and management, and smart urban transport.
- River flow forecasting; emissions and evaporation modelling; prediction of compressive strength of concrete; and forecasting of traffic conditions for proactive management of traffic congestion.
- The smart and connected infrastructure paradigm
- Fundamentals of data engineering
- Python programming for effective data analysis and visualisation
- Fundamentals of MATLAB programming and machine learning applications
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.