Data analytics is an essential skill in modern engineering where all disciplines now involve large-scale data collection and digitalisation. This paper will equip final-year undergraduate students in civil engineering with data analytics skills to handle real-world data challenges. It does not require any in-depth prior knowledge in programming or data analytics but will provide students with the knowledge of the popular tools and the critical thinking skills to apply the core theories in data analytics to solve a range of engineering problems with elements of ambiguity and complexity.
For real-world relevance, the course will utilise real datasets from a range of New Zealand infrastructure owners/operators such as local councils, and government departments. These will further be accompanied by relevant guest lectures from industry and academia. Upon finishing the course, the students will feel confident writing small programs to analyse large datasets, identify patterns and insights from the data that will improve the decision-making process and choose the right tools, algorithms and methods to solve common data analytics problems in civil engineering.
Transport models are powerful tools for assessing the impact of transport infrastructure options and identifying how the transport system is likely to perform in future. During this final year elective course, students will be introduced to the fundamental concepts in planning, modelling, design and operation of transport systems. Students will learn how to model, design and manage road networks based on fundamental modelling concepts, NZ specifications and international best practices.
The course consists of lectures, a weekly clinic/tutorial, computer laboratory works and field exercises for project work. In addition, students will be expected to learn from additional reading, problem-solving (vital) and other work outside formal contact hours. The course is well supported by the recommended texts, which also provide a good source of additional problems.