Learn from experts in data analytics, economics, system engineering, operation research, transport, modelling and simulation, water and energy.

Short courses, the SMART way

SMART partners with Schools and Faculties across campus so the public have access to university-quality content outside of the regular degree programs.

The courses are high-quality educational experiences and delivered through a range of options, including interactive online classes, on-demand and face-to-face. Duration of a short course can vary. Please refer to each course for more details.

All successful participants will receive two University of Wollongong credit points.

Upcoming Courses

Course Overview

With more than 30 Billion connected devices expected by the end of 2020, the Internet of Things (IoT) is radically changing the technological landscapes. Applications opportunities are endless: home automation, healthcare, predictive maintenance, agriculture, energy management, or transportation are only some of those use cases. However, the Internet of Things 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 not only the theoretical background and current usages, but also providing practical knowledge through hands-on tutorials and workshops. Students will gain expertise on the whole IoT process.

Course Outline

This 3-day (21 hours, 6 sessions) course will be delivered face-to-face.


Day 1 (9:30am-5:30pm) — What is the Internet of Things and why should we care?


● Introduction
● Defining the Internet of Things: history, technologies, trends, impacts, and business opportunities
● IoT networks, protocols and interoperability
● Introduction to sensors
● Introduction to IoT development kits: Arduino, LoPy, Raspberry Pi
● A first experiment: Building your first sensor and visualize data


Day 2 (9:30am-5:30pm) — LoRaWAN and The Things Network


● Achieving Long Range and Low Power data transmission
● LoRaWAN Architecture
● The Tings Network – A free to use and open LoRaWAN network
● Tutorial/Workshop: Connecting your sensors to The Things Network
● Managing payloads: Encoding and decoding messages
● Tutorial: Connecting your sensor to the cloud and building your first dashboard with Cayenne


Day 3 (9:30am-5:30pm) — Dashboards and building advanced applications


● Publishing data: MQTT
● Tutorial/Workshop: Graphically build your IoT Application with Node-Red
● Real-world applications (SMART Pedestrians, SMART Storm waterways management)
● Hackathon session

 

Course Benifits

By the end of this course, you will be able to:


● Understand the Internet of Things and its applications
● Discover hardware for the Internet of Things
● Know the different network protocols for the Internet of things
● Have extended knowledge on LoRaWAN
● Deploy and connect sensors to the Things Network
● Build IoT applications

 

Course Type

Introductory course: introducing concepts, methods or tools to relevant students or professionals.

 

Course Pre-Requisite

 Basic knowledge of Computer Science and programming is preferred but not required.

 

Assessment

A small IoT project will be given, and individual report needs to be submitted upon completion of this course. The goal
of this assignment is to detail at a high-level an original IoT solution to solve a real-world problem.

Course Conveners

Dr Johan Barthélemy is a lecturer in the area of Edge-computing, AI and Agent-based Modelling and director of the Digital Living Lab. During his PhD he developed the foundations of a parallelized micro-simulation platform for population and mobility behaviour and applied it to the Belgian context. At SMART he applies his experiences in agent-based simulations and high-performance scientific computing to develop new connected applications. He is also the director of the SMART IoT Hub.

 

Download the course outline

Fees and Discounts

The standard registration fee for this course is $1,800. However, discount codes are available for the following:

  • 10% discount for UOW staff, students and alumni
  • 10% discount for 2 or more course enrolments from the same person
  • 10%, 15%, 20% discount for group booking with 5-9, 10-15 and 15+ enrolments, respectively, from the same organisation.

 

Location

Computer Lab, Ray Cleary building, the University of Wollongong Shoalhaven Campus.

Contact 

For any inquiry please contact: Bobby Du
Coordinator of SMART Teaching Program
Tel: +61 2 4239-2270
Email: bdu@uow.edu.au 

 

This course has been postponed until further notice.

Certification

The students will be provided with 50% discount voucher to undertake AWS certified cloud practitioner exam. Upon passing the exam, the students will get certified by AWS.

Course Overview 

AWS Academy Cloud Foundations is intended for students who seek an overall understanding of cloud computing concepts, independent of specific technical roles. It provides a detailed overview of cloud concepts, AWS core services, security, architecture, pricing, and support. 

Course Outline 

This 3-day (21 hours) short course will be delivered online in real time with recordings available after each session. 

Day 1 – Welcome and AWS Cloud Overview 

Module 0 – Welcome and Introduction
Module 1 - Cloud Concepts Overview
Module 2 - Cloud Economics and Billing
Module 3 - AWS Global Infrastructure Overview

Days 2 – AWS Core Services 

Module 4 - Cloud Security
Module 5 – Networking and content delivery
Module 6 – Compute

Day 3 – Cloud Storage, databases, and Architecture 

Module 7 - Storage
Module 8 - Databases
Module 9 - Cloud Architecting

Course Benefits 

By the end of this course you will be able to: 

  • Define the AWS Cloud
  • Explain the AWS pricing philosophy
  • Identify the global infrastructure components of AWS 
  • Describe the security and compliance measures of the AWS Cloud, including AWS Identity and Access Management (IAM)
  • Create a virtual private cloud (VPC) by using Amazon Virtual Private Cloud (Amazon VPC) 
  • Demonstrate when to use Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda, and AWS Elastic Beanstalk
  • Differentiate between Amazon Simple Storage Service (Amazon S3), Amazon Elastic Block Store (Amazon EBS), Amazon Elastic File System (Amazon EFS), and Amazon Simple Storage Service Glacier (Amazon S3 Glacier) 
  • Demonstrate when to use AWS database services, including Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, Amazon Redshift, and Amazon Aurora
  • Explain the architectural principles of the AWS Cloud
  • Explore key concepts related to Elastic Load Balancing, Amazon CloudWatch, and Amazon EC2 Auto Scaling 

Course Type 

Introductory courseThis is an introductory (level 100) course and is intended for AWS Academy member institutions. 

Course Pre-Requisite 

This is an entry-level course, but students should possess: 

  • General IT technical knowledge 
  • General IT business knowledge 

Assessment 

The assessment comprises of a combination of in-class quizzes (20% weight) and a final assessment to write a short research report (maximum 500 words, 80% weightoutlining the design of a cloud-based solution for an application of your choice and its critical evaluation, in terms of availability, performance, reliability and security. 

 

 

Course Conveners

Dr Mehrdad Amirghasemi is a PhD graduated from the University of Wollongong in Computing and Information TechnologyHe has over four years’ experience in software development industry and is an amazon web services (AWS) certified educator and associateHe has been involved in design and implementation of several cloud-based solutions, such as Enterprise Meta-moderation of Innovation (EMI), a platform that enables organizations to leverage a meta-moderation system to propose, rank, and analyse new ideas and innovations, which has been successfully deployed for MTR Hong Kong. Hhabeen involved in an industry engagement grant with AcrossTheCloud Pty Ltd for the TBA21 ocean archive (OA) platform. OA is a place for collecting and cataloguing ocean-focused media and data coming out of commissions, exhibitions, publications and other TBA21-Academy programmes and alongside other people’s data.

Download the course outline

 

AWS Academy Logo

Fees and Discounts

The standard registration fee for this course is $1,800. However, discount codes are available for the following:

  • 10% discount for UOW staff, students and alumni
  • 10% discount for 2 or more course enrolments from the same person
  • 10%, 15%, 20% discount for group booking with 5-9, 10-15 and 15+ enrolments, respectively, from the same organisation.

 

Contact 

For any inquiry please contact: Bobby Du
Coordinator of SMART Teaching Program
Tel: +61 2 4239-2270
Email: bdu@uow.edu.au 

 

This course has been postponed until further notice.

Course Library

Course Overview

The evolution of Systems Engineering into using model-based tools has resulted in a range of benefits from greater traceability of information within a project to better informed decisions.  Model-based Systems Engineering (MBSE) is a refinement of the traditional Systems Engineering practice to bring the information into a common model.  To support MBSE, the Object Management Group (OMG) and the International Council of Systems Engineers (INCOSE) collaborated to develop the Systems Modelling Language (SysML). 

This course will introduce the MBSE concepts utilising SysML as the modelling paradigm.  The course subsequently expands upon the understanding of SysML to see how broader and more complicated concepts are captured.

Introductory course: introducing concepts, methods or tools to relevant students or professionals

Course Outline

21 hours over 6 sessions

Course Benefits By the end of this course you will appreciate:

  • What is MBSE and how it differs from traditional SE
  • How to read SysML and the nuances inside the language
  • How to utilise the tools to capture more complicated concepts such as risk and human factors

Course Pre-Requisite A knowledge of Systems Engineering principles is preferred but not required.

Session 1 + 2 – Introduction and Physical Modelling

  • Overview of MBSE and SysML
  • Knowledge Management Support for Systems Engineering
  • Physical Structure Modelling

Session 3 + 4 – Behaviour and Requirements

  • Activity and State Modelling
  • Dedicated Views and Model Usability
  • Requirements Representations
  • Allocation between the Pillars of SysML

Session 5 + 6 – Broadening the Scope

  • Introduction to Architecture Frameworks
  • Risk and Safety Modelling
  • Modelling Human Factors

Course Conveners

Dr William Scott is a Chartered Engineer with a PhD in Systems Engineering as well as being internationally certified both as a Certified Systems Engineering Professional (CSEP) through INCOSE and as an Advanced Systems Modeller (OCSMP- Advanced) through the Object Management Group (OMG). Since the 1990s, he has been engaged in modelling and simulation activities that aim to enhance system engineering activities largely in Australian Defence and heavy rail as well as other areas such as hospital operations. He has extensive experience in tailoring and utilizing best practice to aid industry and develop new concepts to ensure that models meet the stakeholder needs.

Grace Kennedy is an Associate Research Fellow in the SMART Infrastructure Facility at UOW.  She has a MEng in Systems Engineering from Loughborough University and specialises in Human Factors, Organisational Systems & Modelling. Grace is a CPEng with specialization in the area of SE and holds CSEP through INCOSE. Prior to emigrating to Australia, she worked in the UK Defence industry.  She is currently undertaking her PhD looking at applications of MBSE on Socio Technical Systems for organisational change.

Dr Farid Shirvani is a research fellow and lecturer in SMART Infrastructure Facility at University of Wollongong. He has a PhD in Model Based Systems Engineering specialises in procurement of complex infrastructure systems. He is OMG Certified Expert in System Modelling (OCSMP) as well as a Microsoft Certified Systems Engineer. Farid has been involved in a variety of systems modelling and systems analysis projects for Transport for NSW, Sydney Trains and ACRI (Australasian Centre of Rail Innovations). His research and teaching interest are model based systems engineering, metamodeling, domain specific languages, and knowledge and information management.

 

 

 

Enrol Now

  • Course dat: TBC
  • This course will run over two weeks on Monday, Wednesday and Friday from 1.30pm to 5pm
  • Delivered online in real time with recordings available after each session
  • Cost $1800

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.

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.

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. 

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. 

 

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.  

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.