This unit is an essential starting point for software developers, IT consultants, and computer scientists to build their understanding of principle computer operation. Students will obtain knowledge and skills with procedural programming. Crucial concepts include defining data types, control flow, iteration, functions, recursion, the model of addressable memory. Students will be able to reinterpret a general problem into a computer problem, and use their understanding of the computer model to develop source code. This unit trains students with software development process, including skills of testing and debugging. It is a prerequisite for more advanced programming languages, systems programming, computer security and high performance computing.

This unit provides both a conceptual and an empirical foundation for the analysis of relationships between society, the environment and natural resources. In our recent past the rapid rate of global environmental change has necessitated a breakdown of traditional disciplinary boundaries in research and social scientists are increasingly called upon to work alongside natural scientists in unraveling the complexities of the human-environmental nexus. Students will examine a number of environmental issues and consider a variety of social science academic perspectives about environmental management.

Students should achieve an understanding of dynamical systems methods applied to complex adaptive systems (CAS). CAS is a new approach to engineering and management that studies and models how relationships between parts give rise to collective and dynamic system-level behaviours, for example, in communication and transport networks, megaprojects, social and eco-systems. Effectively implemented, the methods can dramatically improve a manager's effectiveness in today's complex and interconnected business world, by helping to predict and evaluate indirect effects of actions and policies. This course provides managers with many practical quantitative tools to enhance individual, team, and organisational learning, change, and performance.

The aim of the unit is to introduce students to basic statistical concepts and methods for further studies. Particular attention will be paid to the development of methodologies related to statistical data analysis and Data Mining. A number of useful statistical models will be discussed and computer oriented estimation procedures will be developed. Smoothing and nonparametric concepts for the analysis of large data sets will also be discussed. Students will be exposed to the R computing language to handle all relevant computational aspects in the course.

Globalisation, rapid technological advances, the development of integrated and distributed systems, cross-disciplinary technical collaboration, and the emergence of "evolved" (as opposed to designed) systems are some of the reasons why many systems have begun to be described as complex systems in recent times. Complex technological, biological, socio-economic and socio-ecological systems (power grids, communication and transport systems, food webs, megaprojects, and interdependent civil infrastructure) are composed of large numbers of diverse interacting parts and exhibit self-organisation and/or emergent behaviour. This unit will introduce the basic concepts of "complex systems theory", and focus on methods for the quantitative analysis and modelling of collective emergent phenomena, using diverse computational approaches such as agent-based modelling and simulation, cellular automata, bio-inspired algorithms, and game theory. Students will gain theoretical knowledge of complex adaptive systems, coupled with practical skills in computational simulation and forecasting using a range of modern toolkits.

Our modern day civil infrastructure includes transport networks, telecommunications, power systems, financial infrastructure and emergency services, all of which are growing more and more interconnected. Moreover, the behaviour of the modern infrastructure is not dependent only upon the behaviour of its parts: complex civil systems (such as modern power grids), communication and transport systems, megaprojects, social and eco-systems, generate rich interactions among the individual components with interdependencies across systems. This interdependent behaviour brings about significant new challenges associated with the design and management of complex systems. Cascading power failures, traffic disruptions, epidemic outbreaks, chronic diseases, financial market crashes, and ecosystem collapses are typical manifestations of these challenges, affecting the stability of modern society and civil infrastructure. This unit will develop an understanding of how interdependent systems perform under stress, how to improve resilience and how best to mitigate the effects of various kinds of component failure or human error, by more accurate analysis of interdependent cascades of failures across system boundaries. The studied topics will include dynamical analysis of complex interdependent networks, local and global measures of network structure and evolution, cascading failures, as well as predictive measures of catastrophic failure in complex adaptive systems, and the tools that enable planning for resilient infrastructure. This unit will equip future professionals with sufficient expertise and technical know-how for the design of efficient prevention and intervention policies, and robust crisis forecasting and management. This unit will equip future professionals with sufficient expertise and technical know-how for the design of efficient prevention and intervention policies, and robust crisis forecasting and management.

The dynamics of complex systems are often described in terms of how they process information and self-organise; for example regarding how genes store and utilise information, how information is transferred between neurons in undertaking cognitive tasks, and how swarms process information in order to collectively change direction in response to predators. The language of information also underpins many of the central concepts of complex adaptive systems, including order and randomness, self-organisation and emergence. Shannon information theory, which was originally founded to solve problems of data compression and communication, has found contemporary application in how to formalise such notions of information in the world around us and how these notions can be used to understand and guide the dynamics of complex systems.
This unit of study introduces information theory in this context of analysis of complex systems, foregrounding empirical analysis using modern software toolkits, and applications in time-series analysis, nonlinear dynamical systems and data science. Students will be introduced to the fundamental measures of entropy and mutual information, as well as dynamical measures for time series analysis and information flow such as transfer entropy, building to higher-level applications such as feature selection in machine learning and network inference. They will gain experience in empirical analysis of complex systems using comprehensive software toolkits, and learn to construct their own analyses to dissect and design the dynamics of self-organisation in applications such as neural imaging analysis, natural and robotic swarm behaviour, characterisation of risk factors for and diagnosis of diseases, and financial market dynamics.

Criticality is one of the most important properties of Complex Systems. Criticality occurs in two related but distinct ways: 1. when a system unexpectedly collapses from one state to another, very different, state and 2. when a system is in a state with wild fluctuations and it is highly sensitive to small changes in behaviours. In the first case we call it a 'Tipping Point' and in the second case a 'Continuous Critical Transition'. There are many practical examples of these types of behaviour: Financial markets are often in a continuous critical transition and they can also quickly collapse, diseases that are transferred through a social network can suddenly explode into a pandemic, and a local power outage in an electricity network can cause entire cities to blackout. We will also look at selforganised criticality, where a system evolves to be near one of these 'dangerous' critical points, this is one of the most exciting emergent phenomena in modern applied sciences, engineering and business and we will cover present several real-world applications in this area. This unit will study a range of important examples in which criticality plays a key role and we will show what the underlying causes are for these uncontrolled collapses and wild dynamics. We will use a combination of software examples (Matlab) and mathematical techniques in order to illustrate when and how such interactions might occur and how to simulate their dynamics. It will cover crossdisciplinary concepts and methods based on nonlinear dynamics, including elements of chaos theory and statistical physics, such as fractals and percolation.

Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.

The growing connected-ness of modern society translates into simplifying global communication and accelerating spread of news, information and epidemics. The focus of this unit is on the key concepts to address the challenges induced by the recent scale shift of complex networks. In particular, the course will present how scalable solutions exploiting graph theory, sociology and probability tackle the problems of communicating (routing, diffusing, aggregating) in dynamic and social networks.

The capstone project aims to provide students with the opportunity to carry out a defined piece of independent workplace related research and assessment in a way that fosters the development of practical research skills relevant to Complex Systems. Students will work individually or in small groups on an assigned project, focussed on modelling a complex problem or delivering a novel solution. The concepts covered depend on the nature of the project. The project could be directly tied to student's area of specialisation (major), or to their vocational objectives or interests. Students with expertise in a specific industry sector may be invited to partner with relevant team projects. The project outcomes will be presented in a report that is clear, coherent and logically structured. The project will be judged on the extent and quality of the student's original work and particularly how critical, perceptive and constructive they have been in assessing their work and that of others, in integrating cross-disciplinary complex systems concepts. Students will also be required to present the results of their findings to their peers and supervisors either face to face or by production of a video or other recorded presentation. The skills acquired will be invaluable to students progressing their careers in major multi-national research and development companies, government and crisis management agencies, and large health, construction and transport organisations. Students are expected to take the initiative when pursuing their capstone projects.

Virtually every aspect of a chemical engineer's professional life will involve some use of mathematical techniques. Not only is the modern chemical engineer expected to be proficient in the use of these techniques, they are also expected to be able to utilise computer-based solutions when analytical solutions are unfeasible. This unit of study aims to expose students to an appropriate suite of techniques and enable them to become proficient in the use of mathematics as a tool for the solution of a diversity of chemical engineering problems.
Specifically, this unit consists of two core modules: MODULE A: Applied Statistics for Chemical Engineers and MODULE B: Applied Numerical Methods for Chemical Engineers. These modules aim at furthering knowledge by extending skills in statistical analysis and Chemical Engineering computations. This unit will also enable the development of a systematic approach to solving mathematically oriented Chemical Engineering problems, which will help with making sound engineering decisions.
In addition, there will be considerable time spent during the semester on advanced topics related to mathematical analysis techniques in engineering and recent associated developments.

This unit assesses the political and security significance of disease-related events and developments. Whether one contemplates historical experiences with smallpox, the contemporary challenges posed by diseases such as HIV/AIDS and SARS, or the risks arising from new scientific developments such as synthetic biology, it is clear that diseases exercise a powerful influence over civilised humankind. The unit concentrates on areas in which human health and security concerns intersect most closely, including: biological weapons; fast-moving disease outbreaks of natural origin; safety and security in microbiology laboratories; and the relationships between infectious disease patterns, public health capacity, state functioning and violent conflict. The overall aim of the unit is to provide students with a stronger understanding of the scientific and political nature of these problems, why and how they might threaten security, and the conceptual and empirical connections between them.

Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining.
Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.

The research pathway project aims to provide: (a) analytical and computational skills for modelling systems characterised by many interacting heterogeneous variables, (b) adequate programming skills for simulating complex systems. It is aimed at developing a pathway to a research career. The student will work individually on an assigned open-ended research project, focussed on modelling a complex problem or delivering a novel solution. The concepts covered depend on the nature of the project. The project could be directly tied to student's area of specialisation (major), or to their vocational objectives or interests. Students with expertise in a specific industry sector may be invited to partner with relevant team projects. The project outcomes will be presented in a thesis that is clear, coherent and logically structured. The project will be judged on the extent and quality of the student's original work and particularly how innovative, perceptive and constructive they have been in developing and applying cross-disciplinary complex systems concepts. As the result, the student will develop capability for modelling complex systems, from the identification of the relevant variables and interactions to the analysis and simulations of the predictions, having learnt the conceptual and methodological tools (techniques and algorithms) for the analysis and inference of complex models.

The research pathway project aims to provide: (a) analytical and computational skills for modelling systems characterised by many interacting heterogeneous variables, (b) adequate programming skills for simulating complex systems. It is aimed at developing a pathway to a research career. The student will work individually on an assigned open-ended research project, focussed on modelling a complex problem or delivering a novel solution. The concepts covered depend on the nature of the project. The project could be directly tied to student's area of specialisation (major), or to their vocational objectives or interests. Students with expertise in a specific industry sector may be invited to partner with relevant team projects. The project outcomes will be presented in a thesis that is clear, coherent and logically structured. The project will be judged on the extent and quality of the student's original work and particularly how innovative, perceptive and constructive they have been in developing and applying cross-disciplinary complex systems concepts. As the result, the student will develop capability for modelling complex systems, from the identification of the relevant variables and interactions to the analysis and simulations of the predictions, having learnt the conceptual and methodological tools (techniques and algorithms) for the analysis and inference of complex models.

Data science is a new, rapidly expanding field. There is an unprecedented demand from technology companies, financial services, government and not-for-profits for graduates who can effectively analyse data. This subject will help students gain a critical understanding of the strengths and weaknesses of quantitative research, and acquire practical skills using different methods and tools to answer relevant social science questions.
This subject will offer a nuanced combination of real-world applications to data science methodology, bringing an awareness of how to solve actual social problems to the Master of Data Science. We cover topics including elections, criminology, economics and the media. You will clean, process, model and make meaningful visualisations using data from these fields, and test hypotheses to draw inferences about the social world.
Techniques covered range from descriptive statistics and linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks, to the analysis of unstructured text as data, multilevel and geospatial modelling, all using the open source program R. In doing this, not only will we build on the skills you have already mastered through this degree, but explore different ways to use them once you graduate.

This unit aims to give students an introduction to the planning and operation of modern electricity grids, also known as "smart" grids. Traditional power networks featured a small number of large base-load plants sending power out over transmission lines to be distributed in radial lower voltage networks to loads. In response to the need to reduce carbon impact, future networks will feature diverse generation scattered all over the network including at distribution levels. Also there will be new loads such as electric vehicles and technologies including energy storage and lower voltage power flow control devices. The operation of these new networks will be possible by much greater use of information and communication technology (ICT) and control over the information networks.
The unit will cover recent relevant developments in energy technologies as well as important components of 'smart grids' such as supervisory control and data acquisition (SCADA), substation automation, remote terminal units (RTU), sensors and intelligent electronic devices (IED). Operation of these electricity grids requires a huge amount of data gathering, communication and information processing. The unit will discuss many emerging technologies for such data, information, knowledge and decision processes including communication protocols and network layouts, networking middleware and coordinated control. Information systems and data gathering will be used to assess key performance and security indicators associated with the operation of such grids including stability, reliability and power quality.

This unit of study serves as an introduction to communications network research. The unit relies on a solid understanding of data communications and mobile networks. It introduces some of the currently most debated research topics in mobile networking and presents an overview of different technical solutions. Students are expected to critically evaluate these solutions in their context and produce an objective analysis of the advantages/disadvantages of the different research proposals. The general areas covered are wireless Internet, mobility management, quality of service in mobile and IP networks, ad hoc networks, and cellular network architectures.
The following topics are covered. Introduction to wireless and mobile Internet. Wireless cellular data networks. Cellular mobile networks. Mobile networks of the future. Quality of service in a mobile environment. Traffic modelling for wireless Internet. Traffic management for wireless Internet. Mobility management in mobile networks. Transport protocols for mobile networks. Internet protocols for mobile networks.

Objectives: How to apply the software package Matlab to achieve engineering solutions; Critical assessment of various computer numerical techniques; Professional project management, teamwork, ethics.
This unit assumes an understanding of the fundamental concepts and building blocks of electrical and electronics circuits. As well as covering the specific topics described in the following paragraphs, it aims to develop skills in professional project management and teamwork and promote an understanding of ethics.
Basic features of Matlab. The Matlab desktop. Interactive use with the command window. Performing arithmetic, using complex numbers and mathematical functions. Writing script and function m-files. Matrix manipulations. Control flow. Two dimensional graphics. Application of Matlab to simple problems from circuit theory, electronics, signals and systems and control. Investigation of the steady state and transient behaviour of LCR circuits.
Matlab based numerical solutions applicable to numerical optimisation, ordinary differential equations, and data fitting. Introduction to symbolic mathematics in Matlab. Applications, including the derivation of network functions for simple problems in circuit analysis. Introduction to the use of Simulink for system modelling and simulation.

This unit of study introduces participants to the power of simulation modelling in understanding and predicting behaviour of natural systems. It covers fundamental concepts, logic, and techniques (including sensitivity analysis), and develops skills in application to environmental problems such as catchment management and population dynamics.

Applied ecologists and managers need a good understanding of quantitative methods for assessing environmental impacts and the effectiveness of management and conservation strategies particularly where background variation (error) is inherently high. This unit is for those without a quantitative ecology background. It will introduce you to quantitative methods in the context of three ecological topics that are globally relevant: (1) Impact assessment where the perturbation is unreplicated, (2) Food security in marine ecosystems, and (3) Conservation and restoration in terrestrial ecosystems. The main question we address is how do we test whether any management action has been effective? Describing and understanding uncertainty will be explained in the context of precautionary principles. Issues about measuring biodiversity and the spatial and temporal problems of ecological systems will be introduced.

This unit of study gives an overview of basic spatial data models, and enables students to understand the use of data from a variety of sources within a geographical information system (GIS). The analysis of spatial data, and its manipulation to address questions appropriate to planning or locational applications, will be addressed, as will the development of thematic maps from diverse data layers.

This unit introduces methods for mapping environmental signatures in coastal and marine systems, using both biogeochemical analysis and GIS technologies. Students will learn, theoretically and practically, how environmental data is collected using a range of different methodologies (field and computer based), and application of this data to understanding landscape processes and quantifying environmental change. Students will acquire skills in applying environmental mapping techniques to interpreting key Earth surface processes and understanding the substantial impacts that humans can have on these, in terms of both contamination and remediation.

Many issues have been identified that are of potential relevance for planning, implementation and execution of an evaluation study in the health and technology innovations. This unit aims to address issues covering all phases of an evaluation study: Preliminary outline, study design, operationalization of methods, planning, execution and completion of the evaluation study. Students completing this unit will have better insights leading to a higher quality of evaluation studies for health technology solutions.
This unit is an important component towards building stronger evidence and thus to progress towards evidence-based health solutions and technology innovations.
Graduates of this unit of study will have a strong interdisciplinary knowledge base, covering diverse areas such as health, economics, health technologies, health informatics, social science and information systems.
Topics areas covered: 1. Economic Aspects of Health Technology Evaluation; 2. The Development of Health Technologies and Health Informatics Evaluation; 3. The Role of Evaluation in the Use and Diffusion of Health Technology.

This unit of study aims to introduce the student to the strategy of the Charles Perkins Centre to ease the burden of obesity, diabetes and cardiovascular disease. While other approaches would focus on these diseases as purely medical conditions this unit will challenge the student to focus on an interdisciplinary approach, bringing together medicine, biological science, psychology, economics, law, agriculture and other disciplines to understand how real world solutions for these diseases might be developed. Students will be exposed to the world-renowned researchers based in the Charles Perkins Centre and will gain insight into the research strategy of the Centre. Students will also have the opportunity to develop a new interdisciplinary project node for the Centre in collaboration with one of our research leaders.

The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.

Logistics and supply chain management functions can account for as much as half of the total costs of running a business. The success of the logistic and supply chain management not only impacts on the profitability of a firm but also has a significant and growing impact on customer experience and satisfaction. Logistics and supply chain management plays a major role in implementing organisational strategy and in many industries has sole responsibility for managing customer service. An understanding of the role of this activity within an organisation and how improving logistics and supply chains can assist business managers to better respond to market opportunities is essential for business students. Students undertaking this unit are given a solid grounding in the language, concepts, techniques and principles that underlie the field of logistics and supply chain management, and how knowledge of these concepts contributes towards a strategically effective and operationally efficient organisation or network of organisations.

An understanding of the role of production and operations management within the context of logistics and supply chain management is essential for business managers to better respond to market opportunities and to manage risks to supply chain sustainability and resilience. The role of the production and operations management team within a supply chain is the design, operation and improvement of the processes and systems through which products are made and delivered. The success of a firm's logistic and supply chain management not only impacts on the profitability of a firm but also has a significant and growing impact on customer experience and satisfaction. Students undertaking this unit are equipped with a solid foundation in the language, concepts, techniques and principles that underlie the field of logistics and supply chain management and how successful production and operations management practices have helped organisations improve the efficiency and effectiveness of their supply chains and create competitive advantage. This unit offers a thorough examination of various production and operations management concepts from a logistics and supply chain perspective. The key teaching topics include operations planning hierarchy, resource management, capacity planning, quality management, retail operations and sourcing decisions.

The amount of data generated within organisations is growing rapidly and the ability of supply chains to harness emerging opportunities and respond to issues of sustainability and resilience relies on the ability of managers to make effective decisions based on the information provided by careful analysis of data. Through this unit students develop a strong understanding of the basic techniques underpinning quantitative analysis for logistics and supply chain management and develop highly marketable skills in spreadsheet modelling and the communication and presentation of data to support management decision making. This unit emphasises the practical aspects of quantitative analysis and students are guided through the basic theories used in decision making. This unit emphasises how the theories are applied in practice, drawing on real world experience in quantitative analysis for logistics and supply chain management. The unit covers demand forecasting, spreadsheet modelling, optimisation of production and distribution using linear programming, simulation and quantitative performance management. The unit also introduces basic statistics and linear regression techniques.

Transport and infrastructure plays an important role both in terms of personal mobility as well as accessibility of businesses and their transportation needs. This unit provides a comprehensive introduction to the role of transportation and infrastructure within the economy. The key concepts and theories needed for management of transport and infrastructure are introduced along with the analysis and problem-solving skills needed for confident decision making. In providing the foundational knowledge for students in transport and infrastructure, the unit also introduces students to the professional communication skills needed. Examples and case studies are drawn from all modes of transport and infrastructure.

Successful supply chain management relies upon informed decision making, which aligns with overall business and supply chain strategies. This unit explores a range of important decisions and equips students with a toolkit of models and analytical methods that can assist in making informed decisions. The first set of decisions concern supply chain design and strategy and includes network design and facility location. These decisions provide structure to the supply chain, set the boundaries within which planning decisions are made, and impact on supply chain performance over the long term. In contrast, planning and operational decisions provide value over the medium and short term. Here, this unit covers production planning and scheduling, including aggregate planning, master production scheduling and material requirements planning. Families of models are introduced to support inventory control decision making, with a focus on appropriate model selection, handling uncertainty in supply and demand, and extending the models to handle multiple echelons.

Large scale, sudden onset disasters strike with little or no warning. In their wake, they leave shattered infrastructure, collapsed services and traumatised populations, while the number of dead, injured and homeless often reaches staggering proportions. Humanitarian aid organisations, such as the Red Cross, Doctors without Borders or Oxfam, to name just a few, are usually amongst the first responders, but depend on extremely agile supply chains to support their worldwide operations. Successful disaster relief missions are characterised by the ability of professionals to cope with time pressure, high uncertainty and unusual restrictions. This unit is designed as an introduction to the coordination and management of humanitarian aid and emergency response logistics. Case studies of real events, such as the 2004 Boxing Day tsunami and the 2010 Haiti earthquake provide the framework for analysis and research, while discussion of operational factors, simulations, workshops and group exercises offer students an interactive learning environment.

This unit provides a basic understanding of the main principles underlying strategic transport models for forecasting, and the knowledge to critically assess forecasts of transport strategies made by transport planners. Students acquire knowledge of strategic forecasting models used by government and consultants as well as the methods to capture travel behaviour such as mode choice and route choice. Simple mathematical models are discussed in detail, along with numerical examples and applications in the Sydney Metropolitan Area, which are used to illustrate the principles of the methods. This unit equips students to build simple transport models in the computer lab using specialised transport planning software used by governments and consultants.

Enterprises can access increasing volumes of spatial data (associated with time and space) drawn from a variety of sources including the internet of things, sensors, mobile phone locations and other diverse and unlinked data sets. Managing these data to create useful management insights is a demanding task, and spatial data analysis presents a unique set of challenges and opportunities. Effective management and analysis of spatial data provides strategic value for organisations, across logistics, transport, marketing and other business functions, allowing enterprises to manage strategic challenges in sustainability and resilience. This unit uses real-world data and problem-based learning to develop hands-on experience with managing, processing and modelling spatial data and ultimately drawing insights for business decisions linked to both distribution and supply chain interactions. Students develop highly marketable skills in spatial data analytics that are transferable across a broad range of industries and sectors. These skills include the ability to generate a range of outputs, including decision support systems, maps and visualisations that effectively communicate complex information to support strategic, tactical and operational decision making. This unit utilises a widely-used spatial software package and introduces Geographic Information Systems (GIS), spatial databases and structured query language (SQL).

This unit of study introduces contemporary topics from Ecological Economics and Sustainability Analysis, such as metrics for measuring sustainability; planetary boundaries and other natural limits; comparisons between ecological and environmental economics; valuing the environment; intergenerational discounting; global inequality with a focus on the climate change debate; and links between theories of well-being, human behaviour, consumerism and environmental impact. This unit includes guest lecturers from industry and research and an excursion. The lectures for this unit include interactive activities and group-exercises on a range of concepts related to Ecological Economics. The unit sets the scene for the more detailed and specific units PHYS5032, PHYS5033, and PHYS5034.

This unit of study offers a practical introduction to quantitative analysis techniques including multiple regression, uncertainty analysis, integration, structural decomposition, and dynamic systems modelling, with a strong emphasis on demonstrating their usefulness for environmental problem-solving. This unit will show students how mathematics can be brought to life when utilised in powerful applications to deal with environmental and sustainability issues. Throughout the unit of study, example applications will be explained, including climate modelling, ecosystem trophic chain analysis, linking household consumption and environmental impact, identifying socio-demographic drivers of environmental change, and the uncovering the effect of land use patterns on threats to species.

Innovation is widely-recognised as a major driver of economic growth. Yet innovation projects can be difficult to manage: they typically involve a high level of uncertainty, and many organisations are unsatisfied with the level of innovation they achieve. In this unit of study, we focus on issues in the management of innovation projects at the individual project level, organisational level and across networks of organisations. Since a systematic approach can and does improve our effectiveness in managing innovation, we begin by exploring several different process models of the stages through which innovation projects are managed. We discuss context and challenges which impact such projects, as well as the concepts of creativity and intellectual property management. Using focused case studies, we analyse best practice in the structures and processes that organisations can provide to enable innovation, as well as to support the search, selection, implementation, dissemination, feedback and evaluation stages of their innovative projects. We also examine the impact of networks on innovation (e.g. collaboration networks), national innovation policies and systems, and trends towards open innovation.

This unit identifies the causes of some well-known disasters (natural, man-made and projects) and reveals what can be learned by being able to think critically and analyse the issues. The aim of this unit is to outline traditional and contemporary theories in emergency response planning; to provide an overall scope of comprehensive emergency planning and the major elements that must be addressed in an Emergency Response Plan. Student outcomes from this unit include: Developing and implementing an Emergency Response Plan; Specific recommendations for the health and safety of emergency response personnel and provides concise information on learning objectives and a review of important concepts.

This unit provides students with core skills in epidemiology, particularly the ability to critically appraise public health and clinical epidemiological research literature regarding public health and clinical issues. This unit covers: study types; measures of frequency and association; measurement bias; confounding/effect modification; randomized trials; systematic reviews; screening and test evaluation; infectious disease outbreaks; measuring public health impact and use and interpretation of population health data. In addition to formal classes or their on-line equivalent, it is expected that students spend an additional 2-3 hours at least each week preparing for their tutorials.

This unit highlights the importance of statistical methods and tools for today's managers and analysts and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

It is now common for businesses to have access to very rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualization and the analysis of business and market data. It provides the tools necessary to extract information required for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to business applications of data mining using modern software tools.

To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this unit, students gain skills required to succeed in today's highly analytical and data-driven economy. The unit introduces the basics of data management, business forecasting, decision trees, logistic regression, and predictive modelling. The unit features corporate case studies and hands-on exercises to demonstrate the concepts presented.

This unit introduces students to the extremely close nexus between human health, demographic change and environmental sustainability issues. This relationship is examined within the context of the three pillars of sustainable development with a focus on achieving equitable outcomes. This unit explores the extent to which environmental changes influence population demographics and health, and the extent to which demographic and secular changes impact on the physical environment. The influence of migration, conflict, food insecurity, droughts, flooding, heat stress, emerging and re-emerging infections and chronic health problems on poverty, ageing and dependency, physical, mental and social health and economic sustainability will be analysed alongside the elements needed to preserve the diversity and functioning of the ecosystem for future human survival. International models and policies for mitigating and/or adapting to the negative consequences of globalisation, urbanisation, overconsumption, and resource depletion will be analysed for their potential benefits and harms to sustainable population growth, optimal health and equitable distribution of essential resources.

This unit assesses the political and security significance of disease-related events and developments. Whether one contemplates historical experiences with smallpox, the contemporary challenges posed by diseases such as HIV/AIDS and SARS, or the risks arising from new scientific developments such as synthetic biology, it is clear that diseases exercise a powerful influence over civilised humankind. The unit concentrates on areas in which human health and security concerns intersect most closely, including: biological weapons; fast-moving disease outbreaks of natural origin; safety and security in microbiology laboratories; and the relationships between infectious disease patterns, public health capacity, state functioning and violent conflict. The overall aim of the unit is to provide students with a stronger understanding of the scientific and political nature of these problems, why and how they might threaten security, and the conceptual and empirical connections between them.

Data science is a new, rapidly expanding field. There is an unprecedented demand from technology companies, financial services, government and not-for-profits for graduates who can effectively analyse data. This subject will help students gain a critical understanding of the strengths and weaknesses of quantitative research, and acquire practical skills using different methods and tools to answer relevant social science questions.
This subject will offer a nuanced combination of real-world applications to data science methodology, bringing an awareness of how to solve actual social problems to the Master of Data Science. We cover topics including elections, criminology, economics and the media. You will clean, process, model and make meaningful visualisations using data from these fields, and test hypotheses to draw inferences about the social world.
Techniques covered range from descriptive statistics and linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks, to the analysis of unstructured text as data, multilevel and geospatial modelling, all using the open source program R. In doing this, not only will we build on the skills you have already mastered through this degree, but explore different ways to use them once you graduate.

This unit of study introduces participants to the power of simulation modelling in understanding and predicting behaviour of natural systems. It covers fundamental concepts, logic, and techniques (including sensitivity analysis), and develops skills in application to environmental problems such as catchment management and population dynamics.

Applied ecologists and managers need a good understanding of quantitative methods for assessing environmental impacts and the effectiveness of management and conservation strategies particularly where background variation (error) is inherently high. This unit is for those without a quantitative ecology background. It will introduce you to quantitative methods in the context of three ecological topics that are globally relevant: (1) Impact assessment where the perturbation is unreplicated, (2) Food security in marine ecosystems, and (3) Conservation and restoration in terrestrial ecosystems. The main question we address is how do we test whether any management action has been effective? Describing and understanding uncertainty will be explained in the context of precautionary principles. Issues about measuring biodiversity and the spatial and temporal problems of ecological systems will be introduced.

This unit of study gives an overview of basic spatial data models, and enables students to understand the use of data from a variety of sources within a geographical information system (GIS). The analysis of spatial data, and its manipulation to address questions appropriate to planning or locational applications, will be addressed, as will the development of thematic maps from diverse data layers.

This unit introduces methods for mapping environmental signatures in coastal and marine systems, using both biogeochemical analysis and GIS technologies. Students will learn, theoretically and practically, how environmental data is collected using a range of different methodologies (field and computer based), and application of this data to understanding landscape processes and quantifying environmental change. Students will acquire skills in applying environmental mapping techniques to interpreting key Earth surface processes and understanding the substantial impacts that humans can have on these, in terms of both contamination and remediation.

Many issues have been identified that are of potential relevance for planning, implementation and execution of an evaluation study in the health and technology innovations. This unit aims to address issues covering all phases of an evaluation study: Preliminary outline, study design, operationalization of methods, planning, execution and completion of the evaluation study. Students completing this unit will have better insights leading to a higher quality of evaluation studies for health technology solutions.
This unit is an important component towards building stronger evidence and thus to progress towards evidence-based health solutions and technology innovations.
Graduates of this unit of study will have a strong interdisciplinary knowledge base, covering diverse areas such as health, economics, health technologies, health informatics, social science and information systems.
Topics areas covered: 1. Economic Aspects of Health Technology Evaluation; 2. The Development of Health Technologies and Health Informatics Evaluation; 3. The Role of Evaluation in the Use and Diffusion of Health Technology.

This unit of study aims to introduce the student to the strategy of the Charles Perkins Centre to ease the burden of obesity, diabetes and cardiovascular disease. While other approaches would focus on these diseases as purely medical conditions this unit will challenge the student to focus on an interdisciplinary approach, bringing together medicine, biological science, psychology, economics, law, agriculture and other disciplines to understand how real world solutions for these diseases might be developed. Students will be exposed to the world-renowned researchers based in the Charles Perkins Centre and will gain insight into the research strategy of the Centre. Students will also have the opportunity to develop a new interdisciplinary project node for the Centre in collaboration with one of our research leaders.

This unit of study introduces contemporary topics from Ecological Economics and Sustainability Analysis, such as metrics for measuring sustainability; planetary boundaries and other natural limits; comparisons between ecological and environmental economics; valuing the environment; intergenerational discounting; global inequality with a focus on the climate change debate; and links between theories of well-being, human behaviour, consumerism and environmental impact. This unit includes guest lecturers from industry and research and an excursion. The lectures for this unit include interactive activities and group-exercises on a range of concepts related to Ecological Economics. The unit sets the scene for the more detailed and specific units PHYS5032, PHYS5033, and PHYS5034.

This unit of study offers a practical introduction to quantitative analysis techniques including multiple regression, uncertainty analysis, integration, structural decomposition, and dynamic systems modelling, with a strong emphasis on demonstrating their usefulness for environmental problem-solving. This unit will show students how mathematics can be brought to life when utilised in powerful applications to deal with environmental and sustainability issues. Throughout the unit of study, example applications will be explained, including climate modelling, ecosystem trophic chain analysis, linking household consumption and environmental impact, identifying socio-demographic drivers of environmental change, and the uncovering the effect of land use patterns on threats to species.

This unit provides students with core skills in epidemiology, particularly the ability to critically appraise public health and clinical epidemiological research literature regarding public health and clinical issues. This unit covers: study types; measures of frequency and association; measurement bias; confounding/effect modification; randomized trials; systematic reviews; screening and test evaluation; infectious disease outbreaks; measuring public health impact and use and interpretation of population health data. In addition to formal classes or their on-line equivalent, it is expected that students spend an additional 2-3 hours at least each week preparing for their tutorials.

This unit introduces students to the extremely close nexus between human health, demographic change and environmental sustainability issues. This relationship is examined within the context of the three pillars of sustainable development with a focus on achieving equitable outcomes. This unit explores the extent to which environmental changes influence population demographics and health, and the extent to which demographic and secular changes impact on the physical environment. The influence of migration, conflict, food insecurity, droughts, flooding, heat stress, emerging and re-emerging infections and chronic health problems on poverty, ageing and dependency, physical, mental and social health and economic sustainability will be analysed alongside the elements needed to preserve the diversity and functioning of the ecosystem for future human survival. International models and policies for mitigating and/or adapting to the negative consequences of globalisation, urbanisation, overconsumption, and resource depletion will be analysed for their potential benefits and harms to sustainable population growth, optimal health and equitable distribution of essential resources.

Virtually every aspect of a chemical engineer's professional life will involve some use of mathematical techniques. Not only is the modern chemical engineer expected to be proficient in the use of these techniques, they are also expected to be able to utilise computer-based solutions when analytical solutions are unfeasible. This unit of study aims to expose students to an appropriate suite of techniques and enable them to become proficient in the use of mathematics as a tool for the solution of a diversity of chemical engineering problems.
Specifically, this unit consists of two core modules: MODULE A: Applied Statistics for Chemical Engineers and MODULE B: Applied Numerical Methods for Chemical Engineers. These modules aim at furthering knowledge by extending skills in statistical analysis and Chemical Engineering computations. This unit will also enable the development of a systematic approach to solving mathematically oriented Chemical Engineering problems, which will help with making sound engineering decisions.
In addition, there will be considerable time spent during the semester on advanced topics related to mathematical analysis techniques in engineering and recent associated developments.

Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining.
Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.

Data science is a new, rapidly expanding field. There is an unprecedented demand from technology companies, financial services, government and not-for-profits for graduates who can effectively analyse data. This subject will help students gain a critical understanding of the strengths and weaknesses of quantitative research, and acquire practical skills using different methods and tools to answer relevant social science questions.
This subject will offer a nuanced combination of real-world applications to data science methodology, bringing an awareness of how to solve actual social problems to the Master of Data Science. We cover topics including elections, criminology, economics and the media. You will clean, process, model and make meaningful visualisations using data from these fields, and test hypotheses to draw inferences about the social world.
Techniques covered range from descriptive statistics and linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks, to the analysis of unstructured text as data, multilevel and geospatial modelling, all using the open source program R. In doing this, not only will we build on the skills you have already mastered through this degree, but explore different ways to use them once you graduate.

This unit aims to give students an introduction to the planning and operation of modern electricity grids, also known as "smart" grids. Traditional power networks featured a small number of large base-load plants sending power out over transmission lines to be distributed in radial lower voltage networks to loads. In response to the need to reduce carbon impact, future networks will feature diverse generation scattered all over the network including at distribution levels. Also there will be new loads such as electric vehicles and technologies including energy storage and lower voltage power flow control devices. The operation of these new networks will be possible by much greater use of information and communication technology (ICT) and control over the information networks.
The unit will cover recent relevant developments in energy technologies as well as important components of 'smart grids' such as supervisory control and data acquisition (SCADA), substation automation, remote terminal units (RTU), sensors and intelligent electronic devices (IED). Operation of these electricity grids requires a huge amount of data gathering, communication and information processing. The unit will discuss many emerging technologies for such data, information, knowledge and decision processes including communication protocols and network layouts, networking middleware and coordinated control. Information systems and data gathering will be used to assess key performance and security indicators associated with the operation of such grids including stability, reliability and power quality.

This unit of study serves as an introduction to communications network research. The unit relies on a solid understanding of data communications and mobile networks. It introduces some of the currently most debated research topics in mobile networking and presents an overview of different technical solutions. Students are expected to critically evaluate these solutions in their context and produce an objective analysis of the advantages/disadvantages of the different research proposals. The general areas covered are wireless Internet, mobility management, quality of service in mobile and IP networks, ad hoc networks, and cellular network architectures.
The following topics are covered. Introduction to wireless and mobile Internet. Wireless cellular data networks. Cellular mobile networks. Mobile networks of the future. Quality of service in a mobile environment. Traffic modelling for wireless Internet. Traffic management for wireless Internet. Mobility management in mobile networks. Transport protocols for mobile networks. Internet protocols for mobile networks.

Objectives: How to apply the software package Matlab to achieve engineering solutions; Critical assessment of various computer numerical techniques; Professional project management, teamwork, ethics.
This unit assumes an understanding of the fundamental concepts and building blocks of electrical and electronics circuits. As well as covering the specific topics described in the following paragraphs, it aims to develop skills in professional project management and teamwork and promote an understanding of ethics.
Basic features of Matlab. The Matlab desktop. Interactive use with the command window. Performing arithmetic, using complex numbers and mathematical functions. Writing script and function m-files. Matrix manipulations. Control flow. Two dimensional graphics. Application of Matlab to simple problems from circuit theory, electronics, signals and systems and control. Investigation of the steady state and transient behaviour of LCR circuits.
Matlab based numerical solutions applicable to numerical optimisation, ordinary differential equations, and data fitting. Introduction to symbolic mathematics in Matlab. Applications, including the derivation of network functions for simple problems in circuit analysis. Introduction to the use of Simulink for system modelling and simulation.

The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.

This unit highlights the importance of statistical methods and tools for today's managers and analysts and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

It is now common for businesses to have access to very rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualization and the analysis of business and market data. It provides the tools necessary to extract information required for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to business applications of data mining using modern software tools.

To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this unit, students gain skills required to succeed in today's highly analytical and data-driven economy. The unit introduces the basics of data management, business forecasting, decision trees, logistic regression, and predictive modelling. The unit features corporate case studies and hands-on exercises to demonstrate the concepts presented.

Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining.
Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.

Data science is a new, rapidly expanding field. There is an unprecedented demand from technology companies, financial services, government and not-for-profits for graduates who can effectively analyse data. This subject will help students gain a critical understanding of the strengths and weaknesses of quantitative research, and acquire practical skills using different methods and tools to answer relevant social science questions.
This subject will offer a nuanced combination of real-world applications to data science methodology, bringing an awareness of how to solve actual social problems to the Master of Data Science. We cover topics including elections, criminology, economics and the media. You will clean, process, model and make meaningful visualisations using data from these fields, and test hypotheses to draw inferences about the social world.
Techniques covered range from descriptive statistics and linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks, to the analysis of unstructured text as data, multilevel and geospatial modelling, all using the open source program R. In doing this, not only will we build on the skills you have already mastered through this degree, but explore different ways to use them once you graduate.

This unit of study serves as an introduction to communications network research. The unit relies on a solid understanding of data communications and mobile networks. It introduces some of the currently most debated research topics in mobile networking and presents an overview of different technical solutions. Students are expected to critically evaluate these solutions in their context and produce an objective analysis of the advantages/disadvantages of the different research proposals. The general areas covered are wireless Internet, mobility management, quality of service in mobile and IP networks, ad hoc networks, and cellular network architectures.
The following topics are covered. Introduction to wireless and mobile Internet. Wireless cellular data networks. Cellular mobile networks. Mobile networks of the future. Quality of service in a mobile environment. Traffic modelling for wireless Internet. Traffic management for wireless Internet. Mobility management in mobile networks. Transport protocols for mobile networks. Internet protocols for mobile networks.

This unit aims to give students an introduction to the planning and operation of modern electricity grids, also known as "smart" grids. Traditional power networks featured a small number of large base-load plants sending power out over transmission lines to be distributed in radial lower voltage networks to loads. In response to the need to reduce carbon impact, future networks will feature diverse generation scattered all over the network including at distribution levels. Also there will be new loads such as electric vehicles and technologies including energy storage and lower voltage power flow control devices. The operation of these new networks will be possible by much greater use of information and communication technology (ICT) and control over the information networks.
The unit will cover recent relevant developments in energy technologies as well as important components of 'smart grids' such as supervisory control and data acquisition (SCADA), substation automation, remote terminal units (RTU), sensors and intelligent electronic devices (IED). Operation of these electricity grids requires a huge amount of data gathering, communication and information processing. The unit will discuss many emerging technologies for such data, information, knowledge and decision processes including communication protocols and network layouts, networking middleware and coordinated control. Information systems and data gathering will be used to assess key performance and security indicators associated with the operation of such grids including stability, reliability and power quality.

The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.

An understanding of the role of production and operations management within the context of logistics and supply chain management is essential for business managers to better respond to market opportunities and to manage risks to supply chain sustainability and resilience. The role of the production and operations management team within a supply chain is the design, operation and improvement of the processes and systems through which products are made and delivered. The success of a firm's logistic and supply chain management not only impacts on the profitability of a firm but also has a significant and growing impact on customer experience and satisfaction. Students undertaking this unit are equipped with a solid foundation in the language, concepts, techniques and principles that underlie the field of logistics and supply chain management and how successful production and operations management practices have helped organisations improve the efficiency and effectiveness of their supply chains and create competitive advantage. This unit offers a thorough examination of various production and operations management concepts from a logistics and supply chain perspective. The key teaching topics include operations planning hierarchy, resource management, capacity planning, quality management, retail operations and sourcing decisions.

The amount of data generated within organisations is growing rapidly and the ability of supply chains to harness emerging opportunities and respond to issues of sustainability and resilience relies on the ability of managers to make effective decisions based on the information provided by careful analysis of data. Through this unit students develop a strong understanding of the basic techniques underpinning quantitative analysis for logistics and supply chain management and develop highly marketable skills in spreadsheet modelling and the communication and presentation of data to support management decision making. This unit emphasises the practical aspects of quantitative analysis and students are guided through the basic theories used in decision making. This unit emphasises how the theories are applied in practice, drawing on real world experience in quantitative analysis for logistics and supply chain management. The unit covers demand forecasting, spreadsheet modelling, optimisation of production and distribution using linear programming, simulation and quantitative performance management. The unit also introduces basic statistics and linear regression techniques.

Transport and infrastructure plays an important role both in terms of personal mobility as well as accessibility of businesses and their transportation needs. This unit provides a comprehensive introduction to the role of transportation and infrastructure within the economy. The key concepts and theories needed for management of transport and infrastructure are introduced along with the analysis and problem-solving skills needed for confident decision making. In providing the foundational knowledge for students in transport and infrastructure, the unit also introduces students to the professional communication skills needed. Examples and case studies are drawn from all modes of transport and infrastructure.

Successful supply chain management relies upon informed decision making, which aligns with overall business and supply chain strategies. This unit explores a range of important decisions and equips students with a toolkit of models and analytical methods that can assist in making informed decisions. The first set of decisions concern supply chain design and strategy and includes network design and facility location. These decisions provide structure to the supply chain, set the boundaries within which planning decisions are made, and impact on supply chain performance over the long term. In contrast, planning and operational decisions provide value over the medium and short term. Here, this unit covers production planning and scheduling, including aggregate planning, master production scheduling and material requirements planning. Families of models are introduced to support inventory control decision making, with a focus on appropriate model selection, handling uncertainty in supply and demand, and extending the models to handle multiple echelons.

Large scale, sudden onset disasters strike with little or no warning. In their wake, they leave shattered infrastructure, collapsed services and traumatised populations, while the number of dead, injured and homeless often reaches staggering proportions. Humanitarian aid organisations, such as the Red Cross, Doctors without Borders or Oxfam, to name just a few, are usually amongst the first responders, but depend on extremely agile supply chains to support their worldwide operations. Successful disaster relief missions are characterised by the ability of professionals to cope with time pressure, high uncertainty and unusual restrictions. This unit is designed as an introduction to the coordination and management of humanitarian aid and emergency response logistics. Case studies of real events, such as the 2004 Boxing Day tsunami and the 2010 Haiti earthquake provide the framework for analysis and research, while discussion of operational factors, simulations, workshops and group exercises offer students an interactive learning environment.

This unit provides a basic understanding of the main principles underlying strategic transport models for forecasting, and the knowledge to critically assess forecasts of transport strategies made by transport planners. Students acquire knowledge of strategic forecasting models used by government and consultants as well as the methods to capture travel behaviour such as mode choice and route choice. Simple mathematical models are discussed in detail, along with numerical examples and applications in the Sydney Metropolitan Area, which are used to illustrate the principles of the methods. This unit equips students to build simple transport models in the computer lab using specialised transport planning software used by governments and consultants.

Enterprises can access increasing volumes of spatial data (associated with time and space) drawn from a variety of sources including the internet of things, sensors, mobile phone locations and other diverse and unlinked data sets. Managing these data to create useful management insights is a demanding task, and spatial data analysis presents a unique set of challenges and opportunities. Effective management and analysis of spatial data provides strategic value for organisations, across logistics, transport, marketing and other business functions, allowing enterprises to manage strategic challenges in sustainability and resilience. This unit uses real-world data and problem-based learning to develop hands-on experience with managing, processing and modelling spatial data and ultimately drawing insights for business decisions linked to both distribution and supply chain interactions. Students develop highly marketable skills in spatial data analytics that are transferable across a broad range of industries and sectors. These skills include the ability to generate a range of outputs, including decision support systems, maps and visualisations that effectively communicate complex information to support strategic, tactical and operational decision making. This unit utilises a widely-used spatial software package and introduces Geographic Information Systems (GIS), spatial databases and structured query language (SQL).

This unit highlights the importance of statistical methods and tools for today's managers and analysts and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

It is now common for businesses to have access to very rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualization and the analysis of business and market data. It provides the tools necessary to extract information required for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to business applications of data mining using modern software tools.

To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this unit, students gain skills required to succeed in today's highly analytical and data-driven economy. The unit introduces the basics of data management, business forecasting, decision trees, logistic regression, and predictive modelling. The unit features corporate case studies and hands-on exercises to demonstrate the concepts presented.

The research pathway project aims to provide: (a) analytical and computational skills for modelling systems characterised by many interacting heterogeneous variables, (b) adequate programming skills for simulating complex systems. It is aimed at developing a pathway to a research career. The student will work individually on an assigned open-ended research project, focussed on modelling a complex problem or delivering a novel solution. The concepts covered depend on the nature of the project. The project could be directly tied to student's area of specialisation (major), or to their vocational objectives or interests. Students with expertise in a specific industry sector may be invited to partner with relevant team projects. The project outcomes will be presented in a thesis that is clear, coherent and logically structured. The project will be judged on the extent and quality of the student's original work and particularly how innovative, perceptive and constructive they have been in developing and applying cross-disciplinary complex systems concepts. As the result, the student will develop capability for modelling complex systems, from the identification of the relevant variables and interactions to the analysis and simulations of the predictions, having learnt the conceptual and methodological tools (techniques and algorithms) for the analysis and inference of complex models.

The research pathway project aims to provide: (a) analytical and computational skills for modelling systems characterised by many interacting heterogeneous variables, (b) adequate programming skills for simulating complex systems. It is aimed at developing a pathway to a research career. The student will work individually on an assigned open-ended research project, focussed on modelling a complex problem or delivering a novel solution. The concepts covered depend on the nature of the project. The project could be directly tied to student's area of specialisation (major), or to their vocational objectives or interests. Students with expertise in a specific industry sector may be invited to partner with relevant team projects. The project outcomes will be presented in a thesis that is clear, coherent and logically structured. The project will be judged on the extent and quality of the student's original work and particularly how innovative, perceptive and constructive they have been in developing and applying cross-disciplinary complex systems concepts. As the result, the student will develop capability for modelling complex systems, from the identification of the relevant variables and interactions to the analysis and simulations of the predictions, having learnt the conceptual and methodological tools (techniques and algorithms) for the analysis and inference of complex models.