Introduction to the principles and practice of dealing with data, including measurement scales, data organisation, summaries, study design and inference. Students will learn how to think critically about the use of data in the public and private sectors, and appraise how results and analyses are presented by outlets such as the media. Emphasis will focus on interpretation and understanding of the appropriate use of data rather than the technical details of performing the analysis.

This subject will broaden students’ understanding of the variety of ways analytics is being used in society and the range of challenges that are associated with its use. It will also introduce students to how analytics may be used to support and drive social and organizational change.

Students will study the different stakeholders who engage in analytics projects, at both individual and group level, identifying the variation that occurs in their perspectives and its consequences for analytics use. Students will also examine the role that analytics plays in organisations and society, as a tool for evidence-based decision making and the evaluation of policies and their impact.

Students will examine professional codes of conduct for the use of analytics, in regard to ethical issues and ways to achieve an appropriate balance between privacy and utility.

Students will also investigate the motivations and ways that analytics is used by organisations to implement change, methods for measuring its impact and potential barriers for successful analytics deployment and service delivery.

Students undertake an original investigation of a topic relevant to analytics and its application. Under the supervision and guidance of an academic researcher, students are required to design and conduct a practice-oriented investigation. This would typically involve a literature review, data collection, data analysis and critical reflection of real-world practice. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal.

The project will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the analytics disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles.

Research will be conducted on a topic of mutual interest under the guidance of an experienced researcher.

Refer to MGMT90250 Applied Analytics Capstone Part 1 for details

Representing Spatial Information is the study of conveying insight gained through geospatial data and information. Upon completion, students will be able to communicate complex relations and insights through visual storytelling and concise graphics.

This subject will introduce students to fundamental concepts in spatial information and provide a practical understanding of the rise of the Smart City and how spatial information can assist in evidenced-based and collaborative decision-making.

Students will also be exposed to a range of digital environments, including open data repositories, urban modelling and visualisation tools and open source geospatial information technologies.

Spatial Analytics is the study of geospatial digital data, information, knowledge and models to understand trends, complexities and inform decision process. The subject explores approaches at the intersection of spatial information, statistics and spatial analytics to further students’ understanding of the built environment.

The new science of cities is driven by the deluge of data that enables the mapping of new geographies that can be explored, analysed and synthesized. Studies of urban settlements require a deeper knowledge of digital data and how to access, interrogate and synthesis such data.

A range of research methods will be taught in combination with case studies to provide fundamental skills in Python, spatial analysis and sharpen critical spatial and analytical thinking. At the end you will have a set of Jupyter Notebooks in Python that can be deployed in future cases such as health, urban planning and real estate for evidence-based decision making.

This subject will focus on developing students’ understanding of a wide variety of strategic and operational business problems and decisions being faced by managers and decision makers in the fields of financial management, human resource management, marketing management, operations management, and international business management. Students will be shown how to use a range of quantitative approaches to analyze business problems and, based on these analyses, make effective decisions. The subject will take descriptive analytic, predictive analytic, and prescriptive analytic approaches. Students will be expected to be able to calculate and manipulate data as well as interpret the results in order to derive and evaluate alternative solutions to typical business problems.

Measurement analytics combines measurement science and validity theory with analytics methods. Its main application is to assess human (or sometimes organisational) performance or attributes, using digital big data and analytical techniques. Use of measurement analytics is appropriate when the objective of the analyst is to build reliable and valid assessments of individuals, especially when attributes or levels of performance can only be inferred, not directly observed, and when results have consequences for the individuals concerned.

There are many applications: in education, to any assessment of competence or understanding; in health and human services, to assessments of patient physiological or psychological status; in the professions and vocations for recruitment, to assessment of complex skills, including in areas such as music, sport, and non-cognitive attributes such as attitudes, values and beliefs; and in situations when automated assessments are generated from games, essays, videos or interviews.

In this subject students will develop an understanding of the rationale for using measurement analytics rather than alternative analytics techniques and become familiar with contemporary and emerging applications. This subject provides students with the ability to assess claims to reliability and validity of analytics-based assessments of attributes or performance of individuals, and provides basic understandings and skills in how to maximize validity using complex digital data.

Text data is a primary form of data and its analysis can provide important insights into the behaviours and needs of individuals and organisations. This subject will introduce students to methods for analyzing text and unstructured data.

The following topics will be covered: introduction to text analytics and distinctive features of text data; text data acquisition and storage; text representations and transforming text for analysis; similarity and clustering for text analysis dimensionality reduction strategies; topic and thematic analysis; text classification; text analytics for information extraction and named entity recognition; multi‐lingual text data; applications of text analytics: question answering, essay grading and sentiment analysis; case studies: clinical notes, learning management systems.

Social networks and social platforms are a widely used technology for connecting individuals and connecting organisations. They can provide key insights into human and organizational behaviours and needs. This subject will introduce students to methods for analyzing data generated by social networks and social platforms.

The following topics will be covered: network structure and semantics, including friend‐follower relationships; social network analysis fundamentals including connectedness, centrality and influence; community detection; social network visualisation methods; combining text and social network analysis; user modelling, including prediction and recommendation strategies; gaining insights into groups of users via clustering/segmentation; trend monitoring in social networks; prediction and anomaly detection in networks; automated social interaction: conversational chatbots and their inferential capabilities and interfaces; case studies in public health surveillance, education and psychology.

Dense data sources such as smartphones, social networks, wearable sensors and the internet of things are being used to provide an unparalleled window into psychological processes as they occur in the real world. In this subject, we will train you in the collection and analysis methods that are applicable to experience sampling data from dense data sources. As the data is often sensitive, we will also explore the security and privacy issues that need to be considered when conducting experience sampling studies.

• Completion of this subject requires each individual student to collect and analyse experience sampling data about themselves - it is not possible to opt out of this activity. This experience sampling data will be confidential to the individual student and will not be visible to others.

The Foundations of Analytics is a core subject in the Health specialisation. It looks at the foundational principles and practice of modern data analytics, including techniques of data manipulation, presentation, and analysis. It’s teaching approach will emphasis interpretation underpinned by an understanding of the appropriate use of data, rather than just disseminating the technical details about performing an analysis. Students will be introduced to probability models used for a continuous response and will learn how to use methods such as linear models, tree-based methods and forecasting. Students will also use statistical software to analyse data.

This subject provides a platform for students to apply their knowledge to designing studies with the aim of investigating topical problems in health and public health. Published guidelines for the reporting of studies will be used to assist with the design and appraisal of studies. Topics covered include: clinical trial design; observational studies including cohort, case-control and ecological; causal diagrams (to identify confounders and selection bias); effect modification; measurement error; external validity of findings; and principles of sample size.

The collection and use of routinely collected data will be considered in health and public health settings. The cultural and ethical considerations of data collection with Indigenous populations will also be discussed.

This subject equips students with the practical skills to apply regression methods to health data using the statistical packages R and Stata, as well as a major emphasis on the interpretation and communication of results. Topics covered include: analysis of continuous outcomes with linear regression; analysis of binary outcomes with logistic and tree-based regression methods; analysis of time-to-event outcomes with Cox and Poisson regression; fitting the aforementioned regression models in the statistical packages R and Stata; interpretation of the different measures of association estimated in each of the regression models; how to adjust for confounding and identify variables that modify measures of association using these regression methods; and purpose of regression modelling (causal vs. predictive).

Introduction to the principles and practice of dealing with data, including measurement scales, data organisation, summaries, study design and inference. Students will learn how to think critically about the use of data in the public and private sectors, and appraise how results and analyses are presented by outlets such as the media. Emphasis will focus on interpretation and understanding of the appropriate use of data rather than the technical details of performing the analysis.

This subject will broaden students’ understanding of the variety of ways analytics is being used in society and the range of challenges that are associated with its use. It will also introduce students to how analytics may be used to support and drive social and organizational change.

Students will study the different stakeholders who engage in analytics projects, at both individual and group level, identifying the variation that occurs in their perspectives and its consequences for analytics use. Students will also examine the role that analytics plays in organisations and society, as a tool for evidence-based decision making and the evaluation of policies and their impact.

Students will examine professional codes of conduct for the use of analytics, in regard to ethical issues and ways to achieve an appropriate balance between privacy and utility.

Students will also investigate the motivations and ways that analytics is used by organisations to implement change, methods for measuring its impact and potential barriers for successful analytics deployment and service delivery.

Students undertake an original investigation of a topic relevant to analytics and its application. Under the supervision and guidance of an academic researcher, students are required to design and conduct a practice-oriented investigation. This would typically involve a literature review, data collection, data analysis and critical reflection of real-world practice. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal.

The project will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the analytics disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles.

Research will be conducted on a topic of mutual interest under the guidance of an experienced researcher.

Refer to MGMT90250 Applied Analytics Capstone Part 1 for details

Measurement analytics combines measurement science and validity theory with analytics methods. Its main application is to assess human (or sometimes organisational) performance or attributes, using digital big data and analytical techniques. Use of measurement analytics is appropriate when the objective of the analyst is to build reliable and valid assessments of individuals, especially when attributes or levels of performance can only be inferred, not directly observed, and when results have consequences for the individuals concerned.

There are many applications: in education, to any assessment of competence or understanding; in health and human services, to assessments of patient physiological or psychological status; in the professions and vocations for recruitment, to assessment of complex skills, including in areas such as music, sport, and non-cognitive attributes such as attitudes, values and beliefs; and in situations when automated assessments are generated from games, essays, videos or interviews.

In this subject students will develop an understanding of the rationale for using measurement analytics rather than alternative analytics techniques and become familiar with contemporary and emerging applications. This subject provides students with the ability to assess claims to reliability and validity of analytics-based assessments of attributes or performance of individuals, and provides basic understandings and skills in how to maximize validity using complex digital data.

Social networks and social platforms are a widely used technology for connecting individuals and connecting organisations. They can provide key insights into human and organizational behaviours and needs. This subject will introduce students to methods for analyzing data generated by social networks and social platforms.

The following topics will be covered: network structure and semantics, including friend‐follower relationships; social network analysis fundamentals including connectedness, centrality and influence; community detection; social network visualisation methods; combining text and social network analysis; user modelling, including prediction and recommendation strategies; gaining insights into groups of users via clustering/segmentation; trend monitoring in social networks; prediction and anomaly detection in networks; automated social interaction: conversational chatbots and their inferential capabilities and interfaces; case studies in public health surveillance, education and psychology.

Representing Spatial Information is the study of conveying insight gained through geospatial data and information. Upon completion, students will be able to communicate complex relations and insights through visual storytelling and concise graphics.

This subject will introduce students to fundamental concepts in spatial information and provide a practical understanding of the rise of the Smart City and how spatial information can assist in evidenced-based and collaborative decision-making.

Students will also be exposed to a range of digital environments, including open data repositories, urban modelling and visualisation tools and open source geospatial information technologies.

Spatial Analytics is the study of geospatial digital data, information, knowledge and models to understand trends, complexities and inform decision process. The subject explores approaches at the intersection of spatial information, statistics and spatial analytics to further students’ understanding of the built environment.

The new science of cities is driven by the deluge of data that enables the mapping of new geographies that can be explored, analysed and synthesized. Studies of urban settlements require a deeper knowledge of digital data and how to access, interrogate and synthesis such data.

A range of research methods will be taught in combination with case studies to provide fundamental skills in Python, spatial analysis and sharpen critical spatial and analytical thinking. At the end you will have a set of Jupyter Notebooks in Python that can be deployed in future cases such as health, urban planning and real estate for evidence-based decision making.

This subject will focus on developing students’ understanding of a wide variety of strategic and operational business problems and decisions being faced by managers and decision makers in the fields of financial management, human resource management, marketing management, operations management, and international business management. Students will be shown how to use a range of quantitative approaches to analyze business problems and, based on these analyses, make effective decisions. The subject will take descriptive analytic, predictive analytic, and prescriptive analytic approaches. Students will be expected to be able to calculate and manipulate data as well as interpret the results in order to derive and evaluate alternative solutions to typical business problems.

Text data is a primary form of data and its analysis can provide important insights into the behaviours and needs of individuals and organisations. This subject will introduce students to methods for analyzing text and unstructured data.

The following topics will be covered: introduction to text analytics and distinctive features of text data; text data acquisition and storage; text representations and transforming text for analysis; similarity and clustering for text analysis dimensionality reduction strategies; topic and thematic analysis; text classification; text analytics for information extraction and named entity recognition; multi‐lingual text data; applications of text analytics: question answering, essay grading and sentiment analysis; case studies: clinical notes, learning management systems.

Dense data sources such as smartphones, social networks, wearable sensors and the internet of things are being used to provide an unparalleled window into psychological processes as they occur in the real world. In this subject, we will train you in the collection and analysis methods that are applicable to experience sampling data from dense data sources. As the data is often sensitive, we will also explore the security and privacy issues that need to be considered when conducting experience sampling studies.

• Completion of this subject requires each individual student to collect and analyse experience sampling data about themselves - it is not possible to opt out of this activity. This experience sampling data will be confidential to the individual student and will not be visible to others.

The Foundations of Analytics is a core subject in the Health specialisation. It looks at the foundational principles and practice of modern data analytics, including techniques of data manipulation, presentation, and analysis. It’s teaching approach will emphasis interpretation underpinned by an understanding of the appropriate use of data, rather than just disseminating the technical details about performing an analysis. Students will be introduced to probability models used for a continuous response and will learn how to use methods such as linear models, tree-based methods and forecasting. Students will also use statistical software to analyse data.

This subject provides a platform for students to apply their knowledge to designing studies with the aim of investigating topical problems in health and public health. Published guidelines for the reporting of studies will be used to assist with the design and appraisal of studies. Topics covered include: clinical trial design; observational studies including cohort, case-control and ecological; causal diagrams (to identify confounders and selection bias); effect modification; measurement error; external validity of findings; and principles of sample size.

The collection and use of routinely collected data will be considered in health and public health settings. The cultural and ethical considerations of data collection with Indigenous populations will also be discussed.

This subject equips students with the practical skills to apply regression methods to health data using the statistical packages R and Stata, as well as a major emphasis on the interpretation and communication of results. Topics covered include: analysis of continuous outcomes with linear regression; analysis of binary outcomes with logistic and tree-based regression methods; analysis of time-to-event outcomes with Cox and Poisson regression; fitting the aforementioned regression models in the statistical packages R and Stata; interpretation of the different measures of association estimated in each of the regression models; how to adjust for confounding and identify variables that modify measures of association using these regression methods; and purpose of regression modelling (causal vs. predictive).

Introduction to the principles and practice of dealing with data, including measurement scales, data organisation, summaries, study design and inference. Students will learn how to think critically about the use of data in the public and private sectors, and appraise how results and analyses are presented by outlets such as the media. Emphasis will focus on interpretation and understanding of the appropriate use of data rather than the technical details of performing the analysis.

This subject will broaden students’ understanding of the variety of ways analytics is being used in society and the range of challenges that are associated with its use. It will also introduce students to how analytics may be used to support and drive social and organizational change.

Students will study the different stakeholders who engage in analytics projects, at both individual and group level, identifying the variation that occurs in their perspectives and its consequences for analytics use. Students will also examine the role that analytics plays in organisations and society, as a tool for evidence-based decision making and the evaluation of policies and their impact.

Students will examine professional codes of conduct for the use of analytics, in regard to ethical issues and ways to achieve an appropriate balance between privacy and utility.

Students will also investigate the motivations and ways that analytics is used by organisations to implement change, methods for measuring its impact and potential barriers for successful analytics deployment and service delivery.

Students undertake an original investigation of a topic relevant to analytics and its application. Under the supervision and guidance of an academic researcher, students are required to design and conduct a practice-oriented investigation. This would typically involve a literature review, data collection, data analysis and critical reflection of real-world practice. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal.

The project will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the analytics disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles.

Research will be conducted on a topic of mutual interest under the guidance of an experienced researcher.

Refer to MGMT90250 Applied Analytics Capstone Part 1 for details

The Foundations of Analytics is a core subject in the Health specialisation. It looks at the foundational principles and practice of modern data analytics, including techniques of data manipulation, presentation, and analysis. It’s teaching approach will emphasis interpretation underpinned by an understanding of the appropriate use of data, rather than just disseminating the technical details about performing an analysis. Students will be introduced to probability models used for a continuous response and will learn how to use methods such as linear models, tree-based methods and forecasting. Students will also use statistical software to analyse data.

This subject provides a platform for students to apply their knowledge to designing studies with the aim of investigating topical problems in health and public health. Published guidelines for the reporting of studies will be used to assist with the design and appraisal of studies. Topics covered include: clinical trial design; observational studies including cohort, case-control and ecological; causal diagrams (to identify confounders and selection bias); effect modification; measurement error; external validity of findings; and principles of sample size.

The collection and use of routinely collected data will be considered in health and public health settings. The cultural and ethical considerations of data collection with Indigenous populations will also be discussed.

This subject equips students with the practical skills to apply regression methods to health data using the statistical packages R and Stata, as well as a major emphasis on the interpretation and communication of results. Topics covered include: analysis of continuous outcomes with linear regression; analysis of binary outcomes with logistic and tree-based regression methods; analysis of time-to-event outcomes with Cox and Poisson regression; fitting the aforementioned regression models in the statistical packages R and Stata; interpretation of the different measures of association estimated in each of the regression models; how to adjust for confounding and identify variables that modify measures of association using these regression methods; and purpose of regression modelling (causal vs. predictive).

Representing Spatial Information is the study of conveying insight gained through geospatial data and information. Upon completion, students will be able to communicate complex relations and insights through visual storytelling and concise graphics.

This subject will introduce students to fundamental concepts in spatial information and provide a practical understanding of the rise of the Smart City and how spatial information can assist in evidenced-based and collaborative decision-making.

Students will also be exposed to a range of digital environments, including open data repositories, urban modelling and visualisation tools and open source geospatial information technologies.

Spatial Analytics is the study of geospatial digital data, information, knowledge and models to understand trends, complexities and inform decision process. The subject explores approaches at the intersection of spatial information, statistics and spatial analytics to further students’ understanding of the built environment.

The new science of cities is driven by the deluge of data that enables the mapping of new geographies that can be explored, analysed and synthesized. Studies of urban settlements require a deeper knowledge of digital data and how to access, interrogate and synthesis such data.

A range of research methods will be taught in combination with case studies to provide fundamental skills in Python, spatial analysis and sharpen critical spatial and analytical thinking. At the end you will have a set of Jupyter Notebooks in Python that can be deployed in future cases such as health, urban planning and real estate for evidence-based decision making.

This subject will focus on developing students’ understanding of a wide variety of strategic and operational business problems and decisions being faced by managers and decision makers in the fields of financial management, human resource management, marketing management, operations management, and international business management. Students will be shown how to use a range of quantitative approaches to analyze business problems and, based on these analyses, make effective decisions. The subject will take descriptive analytic, predictive analytic, and prescriptive analytic approaches. Students will be expected to be able to calculate and manipulate data as well as interpret the results in order to derive and evaluate alternative solutions to typical business problems.

Measurement analytics combines measurement science and validity theory with analytics methods. Its main application is to assess human (or sometimes organisational) performance or attributes, using digital big data and analytical techniques. Use of measurement analytics is appropriate when the objective of the analyst is to build reliable and valid assessments of individuals, especially when attributes or levels of performance can only be inferred, not directly observed, and when results have consequences for the individuals concerned.

There are many applications: in education, to any assessment of competence or understanding; in health and human services, to assessments of patient physiological or psychological status; in the professions and vocations for recruitment, to assessment of complex skills, including in areas such as music, sport, and non-cognitive attributes such as attitudes, values and beliefs; and in situations when automated assessments are generated from games, essays, videos or interviews.

In this subject students will develop an understanding of the rationale for using measurement analytics rather than alternative analytics techniques and become familiar with contemporary and emerging applications. This subject provides students with the ability to assess claims to reliability and validity of analytics-based assessments of attributes or performance of individuals, and provides basic understandings and skills in how to maximize validity using complex digital data.

Text data is a primary form of data and its analysis can provide important insights into the behaviours and needs of individuals and organisations. This subject will introduce students to methods for analyzing text and unstructured data.

The following topics will be covered: introduction to text analytics and distinctive features of text data; text data acquisition and storage; text representations and transforming text for analysis; similarity and clustering for text analysis dimensionality reduction strategies; topic and thematic analysis; text classification; text analytics for information extraction and named entity recognition; multi‐lingual text data; applications of text analytics: question answering, essay grading and sentiment analysis; case studies: clinical notes, learning management systems.

Social networks and social platforms are a widely used technology for connecting individuals and connecting organisations. They can provide key insights into human and organizational behaviours and needs. This subject will introduce students to methods for analyzing data generated by social networks and social platforms.

The following topics will be covered: network structure and semantics, including friend‐follower relationships; social network analysis fundamentals including connectedness, centrality and influence; community detection; social network visualisation methods; combining text and social network analysis; user modelling, including prediction and recommendation strategies; gaining insights into groups of users via clustering/segmentation; trend monitoring in social networks; prediction and anomaly detection in networks; automated social interaction: conversational chatbots and their inferential capabilities and interfaces; case studies in public health surveillance, education and psychology.

Dense data sources such as smartphones, social networks, wearable sensors and the internet of things are being used to provide an unparalleled window into psychological processes as they occur in the real world. In this subject, we will train you in the collection and analysis methods that are applicable to experience sampling data from dense data sources. As the data is often sensitive, we will also explore the security and privacy issues that need to be considered when conducting experience sampling studies.

• Completion of this subject requires each individual student to collect and analyse experience sampling data about themselves - it is not possible to opt out of this activity. This experience sampling data will be confidential to the individual student and will not be visible to others.

Introduction to the principles and practice of dealing with data, including measurement scales, data organisation, summaries, study design and inference. Students will learn how to think critically about the use of data in the public and private sectors, and appraise how results and analyses are presented by outlets such as the media. Emphasis will focus on interpretation and understanding of the appropriate use of data rather than the technical details of performing the analysis.

This subject will broaden students’ understanding of the variety of ways analytics is being used in society and the range of challenges that are associated with its use. It will also introduce students to how analytics may be used to support and drive social and organizational change.

Students will study the different stakeholders who engage in analytics projects, at both individual and group level, identifying the variation that occurs in their perspectives and its consequences for analytics use. Students will also examine the role that analytics plays in organisations and society, as a tool for evidence-based decision making and the evaluation of policies and their impact.

Students will examine professional codes of conduct for the use of analytics, in regard to ethical issues and ways to achieve an appropriate balance between privacy and utility.

Students will also investigate the motivations and ways that analytics is used by organisations to implement change, methods for measuring its impact and potential barriers for successful analytics deployment and service delivery.

Students undertake an original investigation of a topic relevant to analytics and its application. Under the supervision and guidance of an academic researcher, students are required to design and conduct a practice-oriented investigation. This would typically involve a literature review, data collection, data analysis and critical reflection of real-world practice. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal.

The project will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the analytics disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles.

Research will be conducted on a topic of mutual interest under the guidance of an experienced researcher.

Refer to MGMT90250 Applied Analytics Capstone Part 1 for details

Dense data sources such as smartphones, social networks, wearable sensors and the internet of things are being used to provide an unparalleled window into psychological processes as they occur in the real world. In this subject, we will train you in the collection and analysis methods that are applicable to experience sampling data from dense data sources. As the data is often sensitive, we will also explore the security and privacy issues that need to be considered when conducting experience sampling studies.

• Completion of this subject requires each individual student to collect and analyse experience sampling data about themselves - it is not possible to opt out of this activity. This experience sampling data will be confidential to the individual student and will not be visible to others.

Social networks and social platforms are a widely used technology for connecting individuals and connecting organisations. They can provide key insights into human and organizational behaviours and needs. This subject will introduce students to methods for analyzing data generated by social networks and social platforms.

The following topics will be covered: network structure and semantics, including friend‐follower relationships; social network analysis fundamentals including connectedness, centrality and influence; community detection; social network visualisation methods; combining text and social network analysis; user modelling, including prediction and recommendation strategies; gaining insights into groups of users via clustering/segmentation; trend monitoring in social networks; prediction and anomaly detection in networks; automated social interaction: conversational chatbots and their inferential capabilities and interfaces; case studies in public health surveillance, education and psychology.

Representing Spatial Information is the study of conveying insight gained through geospatial data and information. Upon completion, students will be able to communicate complex relations and insights through visual storytelling and concise graphics.

This subject will introduce students to fundamental concepts in spatial information and provide a practical understanding of the rise of the Smart City and how spatial information can assist in evidenced-based and collaborative decision-making.

Students will also be exposed to a range of digital environments, including open data repositories, urban modelling and visualisation tools and open source geospatial information technologies.

Spatial Analytics is the study of geospatial digital data, information, knowledge and models to understand trends, complexities and inform decision process. The subject explores approaches at the intersection of spatial information, statistics and spatial analytics to further students’ understanding of the built environment.

The new science of cities is driven by the deluge of data that enables the mapping of new geographies that can be explored, analysed and synthesized. Studies of urban settlements require a deeper knowledge of digital data and how to access, interrogate and synthesis such data.

A range of research methods will be taught in combination with case studies to provide fundamental skills in Python, spatial analysis and sharpen critical spatial and analytical thinking. At the end you will have a set of Jupyter Notebooks in Python that can be deployed in future cases such as health, urban planning and real estate for evidence-based decision making.

This subject will focus on developing students’ understanding of a wide variety of strategic and operational business problems and decisions being faced by managers and decision makers in the fields of financial management, human resource management, marketing management, operations management, and international business management. Students will be shown how to use a range of quantitative approaches to analyze business problems and, based on these analyses, make effective decisions. The subject will take descriptive analytic, predictive analytic, and prescriptive analytic approaches. Students will be expected to be able to calculate and manipulate data as well as interpret the results in order to derive and evaluate alternative solutions to typical business problems.

Measurement analytics combines measurement science and validity theory with analytics methods. Its main application is to assess human (or sometimes organisational) performance or attributes, using digital big data and analytical techniques. Use of measurement analytics is appropriate when the objective of the analyst is to build reliable and valid assessments of individuals, especially when attributes or levels of performance can only be inferred, not directly observed, and when results have consequences for the individuals concerned.

There are many applications: in education, to any assessment of competence or understanding; in health and human services, to assessments of patient physiological or psychological status; in the professions and vocations for recruitment, to assessment of complex skills, including in areas such as music, sport, and non-cognitive attributes such as attitudes, values and beliefs; and in situations when automated assessments are generated from games, essays, videos or interviews.

In this subject students will develop an understanding of the rationale for using measurement analytics rather than alternative analytics techniques and become familiar with contemporary and emerging applications. This subject provides students with the ability to assess claims to reliability and validity of analytics-based assessments of attributes or performance of individuals, and provides basic understandings and skills in how to maximize validity using complex digital data.

Text data is a primary form of data and its analysis can provide important insights into the behaviours and needs of individuals and organisations. This subject will introduce students to methods for analyzing text and unstructured data.

The following topics will be covered: introduction to text analytics and distinctive features of text data; text data acquisition and storage; text representations and transforming text for analysis; similarity and clustering for text analysis dimensionality reduction strategies; topic and thematic analysis; text classification; text analytics for information extraction and named entity recognition; multi‐lingual text data; applications of text analytics: question answering, essay grading and sentiment analysis; case studies: clinical notes, learning management systems.

The Foundations of Analytics is a core subject in the Health specialisation. It looks at the foundational principles and practice of modern data analytics, including techniques of data manipulation, presentation, and analysis. It’s teaching approach will emphasis interpretation underpinned by an understanding of the appropriate use of data, rather than just disseminating the technical details about performing an analysis. Students will be introduced to probability models used for a continuous response and will learn how to use methods such as linear models, tree-based methods and forecasting. Students will also use statistical software to analyse data.

This subject provides a platform for students to apply their knowledge to designing studies with the aim of investigating topical problems in health and public health. Published guidelines for the reporting of studies will be used to assist with the design and appraisal of studies. Topics covered include: clinical trial design; observational studies including cohort, case-control and ecological; causal diagrams (to identify confounders and selection bias); effect modification; measurement error; external validity of findings; and principles of sample size.

The collection and use of routinely collected data will be considered in health and public health settings. The cultural and ethical considerations of data collection with Indigenous populations will also be discussed.

This subject equips students with the practical skills to apply regression methods to health data using the statistical packages R and Stata, as well as a major emphasis on the interpretation and communication of results. Topics covered include: analysis of continuous outcomes with linear regression; analysis of binary outcomes with logistic and tree-based regression methods; analysis of time-to-event outcomes with Cox and Poisson regression; fitting the aforementioned regression models in the statistical packages R and Stata; interpretation of the different measures of association estimated in each of the regression models; how to adjust for confounding and identify variables that modify measures of association using these regression methods; and purpose of regression modelling (causal vs. predictive).

Introduction to the principles and practice of dealing with data, including measurement scales, data organisation, summaries, study design and inference. Students will learn how to think critically about the use of data in the public and private sectors, and appraise how results and analyses are presented by outlets such as the media. Emphasis will focus on interpretation and understanding of the appropriate use of data rather than the technical details of performing the analysis.

This subject will broaden students’ understanding of the variety of ways analytics is being used in society and the range of challenges that are associated with its use. It will also introduce students to how analytics may be used to support and drive social and organizational change.

Students will study the different stakeholders who engage in analytics projects, at both individual and group level, identifying the variation that occurs in their perspectives and its consequences for analytics use. Students will also examine the role that analytics plays in organisations and society, as a tool for evidence-based decision making and the evaluation of policies and their impact.

Students will examine professional codes of conduct for the use of analytics, in regard to ethical issues and ways to achieve an appropriate balance between privacy and utility.

Students will also investigate the motivations and ways that analytics is used by organisations to implement change, methods for measuring its impact and potential barriers for successful analytics deployment and service delivery.

Students undertake an original investigation of a topic relevant to analytics and its application. Under the supervision and guidance of an academic researcher, students are required to design and conduct a practice-oriented investigation. This would typically involve a literature review, data collection, data analysis and critical reflection of real-world practice. The results will be reported as a thesis and in a public presentation. In some instances, it is expected that the results will also be submitted for publication in a conference or journal.

The project will require an explorative approach, where students will pursue outcomes associated with new knowledge or understanding, within the analytics disciplines, often as an adjunct to existing academic research initiatives. It is expected that the Capstone Project will incorporate findings associated with both well-defined professional practice and research principles.

Research will be conducted on a topic of mutual interest under the guidance of an experienced researcher.

Refer to MGMT90250 Applied Analytics Capstone Part 1 for details

Representing Spatial Information is the study of conveying insight gained through geospatial data and information. Upon completion, students will be able to communicate complex relations and insights through visual storytelling and concise graphics.

This subject will introduce students to fundamental concepts in spatial information and provide a practical understanding of the rise of the Smart City and how spatial information can assist in evidenced-based and collaborative decision-making.

Students will also be exposed to a range of digital environments, including open data repositories, urban modelling and visualisation tools and open source geospatial information technologies.

Spatial Analytics is the study of geospatial digital data, information, knowledge and models to understand trends, complexities and inform decision process. The subject explores approaches at the intersection of spatial information, statistics and spatial analytics to further students’ understanding of the built environment.

The new science of cities is driven by the deluge of data that enables the mapping of new geographies that can be explored, analysed and synthesized. Studies of urban settlements require a deeper knowledge of digital data and how to access, interrogate and synthesis such data.

A range of research methods will be taught in combination with case studies to provide fundamental skills in Python, spatial analysis and sharpen critical spatial and analytical thinking. At the end you will have a set of Jupyter Notebooks in Python that can be deployed in future cases such as health, urban planning and real estate for evidence-based decision making.

This subject will focus on developing students’ understanding of a wide variety of strategic and operational business problems and decisions being faced by managers and decision makers in the fields of financial management, human resource management, marketing management, operations management, and international business management. Students will be shown how to use a range of quantitative approaches to analyze business problems and, based on these analyses, make effective decisions. The subject will take descriptive analytic, predictive analytic, and prescriptive analytic approaches. Students will be expected to be able to calculate and manipulate data as well as interpret the results in order to derive and evaluate alternative solutions to typical business problems.

Measurement analytics combines measurement science and validity theory with analytics methods. Its main application is to assess human (or sometimes organisational) performance or attributes, using digital big data and analytical techniques. Use of measurement analytics is appropriate when the objective of the analyst is to build reliable and valid assessments of individuals, especially when attributes or levels of performance can only be inferred, not directly observed, and when results have consequences for the individuals concerned.

There are many applications: in education, to any assessment of competence or understanding; in health and human services, to assessments of patient physiological or psychological status; in the professions and vocations for recruitment, to assessment of complex skills, including in areas such as music, sport, and non-cognitive attributes such as attitudes, values and beliefs; and in situations when automated assessments are generated from games, essays, videos or interviews.

In this subject students will develop an understanding of the rationale for using measurement analytics rather than alternative analytics techniques and become familiar with contemporary and emerging applications. This subject provides students with the ability to assess claims to reliability and validity of analytics-based assessments of attributes or performance of individuals, and provides basic understandings and skills in how to maximize validity using complex digital data.

Text data is a primary form of data and its analysis can provide important insights into the behaviours and needs of individuals and organisations. This subject will introduce students to methods for analyzing text and unstructured data.

The following topics will be covered: introduction to text analytics and distinctive features of text data; text data acquisition and storage; text representations and transforming text for analysis; similarity and clustering for text analysis dimensionality reduction strategies; topic and thematic analysis; text classification; text analytics for information extraction and named entity recognition; multi‐lingual text data; applications of text analytics: question answering, essay grading and sentiment analysis; case studies: clinical notes, learning management systems.

Social networks and social platforms are a widely used technology for connecting individuals and connecting organisations. They can provide key insights into human and organizational behaviours and needs. This subject will introduce students to methods for analyzing data generated by social networks and social platforms.

The following topics will be covered: network structure and semantics, including friend‐follower relationships; social network analysis fundamentals including connectedness, centrality and influence; community detection; social network visualisation methods; combining text and social network analysis; user modelling, including prediction and recommendation strategies; gaining insights into groups of users via clustering/segmentation; trend monitoring in social networks; prediction and anomaly detection in networks; automated social interaction: conversational chatbots and their inferential capabilities and interfaces; case studies in public health surveillance, education and psychology.

Dense data sources such as smartphones, social networks, wearable sensors and the internet of things are being used to provide an unparalleled window into psychological processes as they occur in the real world. In this subject, we will train you in the collection and analysis methods that are applicable to experience sampling data from dense data sources. As the data is often sensitive, we will also explore the security and privacy issues that need to be considered when conducting experience sampling studies.

• Completion of this subject requires each individual student to collect and analyse experience sampling data about themselves - it is not possible to opt out of this activity. This experience sampling data will be confidential to the individual student and will not be visible to others.

The Foundations of Analytics is a core subject in the Health specialisation. It looks at the foundational principles and practice of modern data analytics, including techniques of data manipulation, presentation, and analysis. It’s teaching approach will emphasis interpretation underpinned by an understanding of the appropriate use of data, rather than just disseminating the technical details about performing an analysis. Students will be introduced to probability models used for a continuous response and will learn how to use methods such as linear models, tree-based methods and forecasting. Students will also use statistical software to analyse data.

This subject provides a platform for students to apply their knowledge to designing studies with the aim of investigating topical problems in health and public health. Published guidelines for the reporting of studies will be used to assist with the design and appraisal of studies. Topics covered include: clinical trial design; observational studies including cohort, case-control and ecological; causal diagrams (to identify confounders and selection bias); effect modification; measurement error; external validity of findings; and principles of sample size.

The collection and use of routinely collected data will be considered in health and public health settings. The cultural and ethical considerations of data collection with Indigenous populations will also be discussed.

This subject equips students with the practical skills to apply regression methods to health data using the statistical packages R and Stata, as well as a major emphasis on the interpretation and communication of results. Topics covered include: analysis of continuous outcomes with linear regression; analysis of binary outcomes with logistic and tree-based regression methods; analysis of time-to-event outcomes with Cox and Poisson regression; fitting the aforementioned regression models in the statistical packages R and Stata; interpretation of the different measures of association estimated in each of the regression models; how to adjust for confounding and identify variables that modify measures of association using these regression methods; and purpose of regression modelling (causal vs. predictive).