Course structure

Course structure

To gain the Graduate Certificate in Applied Analytics, you must complete 50 points comprised of:

  • Two core subjects; and
  • Two elective subjects.
Core subjects
Critical Thinking With Analytics12.5

Critical Thinking With Analytics

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.

Detailed Information MAST90130
Type Compulsory
Analytics and society12.5

Analytics and Society

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 organisational change. 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.

Detailed Information MGMT90248
Type Compulsory
Elective subjects
Foundations of Analytics12.5

Foundations of Analytics

The foundational principles and practice of modern data analytics, including skills in data manipulation, presentation, and analysis; introduction to probability models used for a continuous response. Students will learn how to use methods such as linear models and tree-based methods for forecasting. Students will use statistical software to analyse data. However, emphasis will focus on interpretation and understanding of the appropriate use of data rather than the technical details of performing the analysis.

Detailed Information MAST90135
Type Elective
Advanced Elements of Analytics12.5

Advanced Elements of Analytics

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).

Detailed Information MAST90134
Type Elective
Designing Analytics Investigations12.5

Designing Analytics Investigations

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.

Detailed Information POPH90295
Type Elective
Measurement Analytics12.5

Measurement Analytics

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. 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 candidates 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 maximise validity using complex digital data.

Detailed Information MAST90131
Type Elective
Social Analytics12.5

Social Analytics

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

Detailed Information COMP90076
Type Elective
Representing Spatial Information12.5

Representing Spatial Information

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.

Detailed Information ABPL90407
Type Elective
Spatial Analytics12.5

Spatial Analytics

Spatial Analytics is the study of geospatial digital data, information, knowledge and models to understand trends, complexities and inform decision processes.  This subject explores a range of approaches at the intersection of spatial information, statistics and policy 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. A range of research methods will be considered in combination with case studies to provide fundamental skills in spatial analysis and sharpen critical spatial and geographical thinking. Case studies will be based on contemporary problems in health, urban planning and real estate.

Detailed Information ABPL90408
Type Elective
Introduction to Experience Sampling12.5

Introduction to Experience Sampling

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.

Detailed Information PSYC90109
Type Elective
Text Analytics12.5

Text Analytics

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 analysing text and unstructured data. The following topics will be covered: introduction to text analytics and distinctive features of text datatext 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.

Detailed Information COMP90075
Type Elective
Business Analytics for Decision Making12.5

Business Analytics for 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 analyse 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.

Detailed Information MGMT90239
Type Elective

Please note: students can take subjects from outside of their own specialisation and within other specialisations as an elective.


The estimated hours required for each subject is between 15-19 hours per week, but this varies for each student and depends on your task management and planning, familiarity with the material, reading style and speed.

Entry requirements

Discuss your subjects

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