Short Courses

Short Courses

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Coming in 2019, the SMART Infrastructure Facility will be offering a range of short courses developed based on our interdisciplinary collaboration in a various research fields including data analytics, economics, system engineering, operation research, transport, water, energy and modelling & simulation.

SMART aims to combine our comprehensive and unique research and project experiences with the knowledge behind it to educate through case studies and live projects. SMART's short courses aim to enhance understanding and application of the relevant theories and technologies.

Upcoming short courses can be found below.

Short Course Course Conveners

FEIS801: Big Data Analytics with Application

Jun Ma
Jie (Jack) Yang
Bo (Bobby) Du                                                               

FEIS802: Infrastructure System of Systems Engineering

Ricardo Peculis
Farid Shirvani
William Scott

FEIS803: Introduction to Participatory Modelling

Juan Castilla
Pascal Perez
Shiva Pedram

FEIS804: Introduction to Internet of Things

Nicolas Verstaevel
Johan Barthelemy
Pascal Perez

FEIS805: Computational Methods in Supply Chain and Logistics

Mehrdad Amirghasemi
Johan Barthelemy
Tillman Boehme

FEIS806: Urban Transport Planning for the Digital Age

Bo (Bobby) Du
Cole Hendrigan
Nicolas Verstaevel

FEIS807: Introduction to Agent-Based Modelling of Urban Systems

Johan Barthelemy
Juan Castilla
Nicolas Verstaevel

Short Course Package Brochure
 

 

Overviews of SMART's short courses for 2019 can be found below and in the brochure above. If you are interested in our short courses or would like further information, you can contact us here. More information and registration details coming soon.

Short Course Overviews

 

FEIS801: Big Data Analytics with Application

 

This introductory course on data science covers the following topics: data manipulation, data analysis with statistic and machine learning, data visualisation and how to work with large data sets. These concepts will be illustrated using programming languages often used and freely available, namely R, Python and SQL. The course presents in a practical way multivariate statistical analysis methods such as Regression, Clustering, Principal Component Analysis, Factor Analysis and ANOVA.

  


  

FEIS802: Infrastructure System of Systems Engineering 

Infrastructure systems are sociotechnical systems within an organisational environment. The presence of social and organisational aspects increases complexity and influences these systems throughout their life cycle, from conception and planning, engineering, operation, upgrades and final disposal. Infrastructure systems will be examined as ‘System of Systems’ (SoS). Various approaches for System of Systems Engineering (SoSE) will be presented and discussed. ‘Systems Thinking’, considered the most adequate approach to deal with the complexity of sociotechnical SoS, will be presented and illustrated with practical examples. Designing for Adaptability and evolution in System of Systems Engineering (DANSE) methodology will be introduced. The course will address the fundamentals of modelling and simulation considered to be of great importance for Infrastructure SoSE.

  


 

FEIS803: Introduction to Participatory Modelling

 

If computer models are to be faithful representations of real-world systems, how can we possibly build them without input from the people who actually interact with and form part of systems in reality? This course introduces the use of participatory or collaborative model building to empower audiences to become architects of what would otherwise
be a purely scientific modelling process occurring behind closed doors. Participatory modelling serves as the ‘glue’ for stakeholders to collectively explore the implications of their actions and decisions on social, economic, and environmental outcomes of concern, particularly in those cases where responsibilities and burdens are unclear.

This introductory course will provide researchers, government and industry professionals with the basic knowledge and skills to facilitate collaborative modelling in interdisciplinary and cross-cultural settings. Attendees will gain access to a rich methodological toolbox that can help groups navigate through complex problems, engage in constructive dialogue towards common goals, and identify leverage points for building sustainability and resilience in the systems they need to manage or are part of. Participants will learn how to capture the salient features of a complex system into a coherent and simple but elegant simulation model. 

 


 

FEIS804: Introduction to Internet of Things

With more than 30 billion connected devices expected by 2020, the Internet of Things (IoT) is radically changing the technological landscape. Application opportunities are endless; home automation, healthcare, predictive maintenance, agriculture, energy management, or transportation are some of those use cases. However, IoT is more than just sensors, it’s a process ranging from remote data collection to data analytics in order to grasp the full potential of your data.

This course offers an introduction to the IoT covering the theoretical background and current usages, but also providing practical knowledge through hands-on tutorials and workshops. Attendees will gain expertise on the whole IoT process.

 


 

FEIS805: Computational Methods in Supply Chain and Logistics

 

Today’s world is producing an ever increasing amount of data. Businesses then need data analysis to provide forward-looking guidance that yields better, more-informed decisions. This subject introduces quantitative methods to optimise the decisions to be made in the context of supply chain and logistic systems. Each method will be illustrated with real world case studies. As such, participants will learn to verify and enhance existing operating models.

The course starts with an introduction to supply chain and logistics and, some representative problems with several real-life applications. Effective tools for tackling these problems such as standard mathematical techniques or Linear Programming are explained and their implementation in Microsoft Excel is emphasised. The course is concluded by introducing some advanced metaheuristics, and their implementation in Excel VBA. 

 


 

FEIS806: Urban Transport Planning for the Digital Age

 

Traditional methods for transport planning have been widely used in the past, however more and more transport researchers and planners have realised the shortcomings of the classic methods in the digital age where historical and real-time data from various digital sources, such as GPS, smartphones, smart cards and Bluetooth sensors, are more readily available for better transport planning. Moreover, compared to traditional transport modes (e.g. bike, car, bus and train), more options (like autonomous vehicle, electric vehicles, connected vehicles, and scooters) are likely to emerge providing solutions for unsolved problems as well as posing new challenges in planning for their impacts on the demand for urban transport.

It is necessary to revisit the basics of urban transport planning to understand the effective use of digital data and new technologies and how they can be used to provide smarter mobility solutions.

This short course will provide transport researchers and planners with basic knowledge of the transport planning process, as well as major innovations and changes in the digital age. Real case studies will be shared as references for modern urban transport planning.  

 


 

FEIS807: Introduction to Agent-Based Modelling of Urban Systems 

Societies, modern cities, and urban infrastructure systems are becoming more complex, interconnected, difficult to optimise, control, and manage. Agent-based modelling (ABM) offers a new lens to understand and steer the functioning of these systems by conducting experiments on artificial societies of computer agents.

The course will begin by introducing fundamental principles of complexity and the dynamics of complex adaptive systems. A structured process to conceptualise, design, build, analyse and validate ABMs will then be explained and illustrated using real-world examples. The course will draw on applications in a wide variety of social, urban, and infrastructure problems, to help illustrate the power of ABM as an effective and accessible tool to understand why systems don’t always behave as expected, and what can be done to improve them.  

Team

Bobby Du Headshot

Dr Bo (Bobby) Du

Course Coordinator

Email: bo_du@uow.edu.au
Telephone: +61 2 4239 2270

Profile 

Last reviewed: 14 February, 2019