Enterprise Computing syllabus, dot point by dot point

Explain how blockchain technology is used to manage and verify data, including online voting, online identities, tracking items of value and recordkeeping

Explore the use of big data and data warehousing, considering volume, variety and velocity; explore the risks and benefits of data mining; analyse the impact of data scale, including volume of raw data, storage, real-time and continuous streaming, opportunities for machine learning (ML), changes in human behaviour and ethical implications, including digital footprints

Explore the difference between quantitative and qualitative data; determine which data types are used to represent quantitative and qualitative data; explore nominal, ordinal, interval and ratio levels of measurement applied to data

Investigate data sampling, including manual and computerised methods of active and passive data collection; assess the relevance, accuracy, validity and reliability of primary and secondary data; examine the impact of errors, uncertainty and limitations in data, including data sources, raw data versus processed data and data bias

Investigate how informatics supports the development of a deeper understanding of data; interpret and present data using graphs, infographics, dashboards, reports, network diagrams and maps; investigate structured and unstructured datasets; explore the use of likes, emoticons and memes as forms of alternative data as sources of feedback

Examine software features that affect the privacy and security of data, including autofill, public or private connections, checkbox and terms of agreement

Evaluate the effectiveness of different methods for data storage, including local storage, cloud storage, portable storage media and data warehouses

Explore how machine learning and statistical modelling are used in data analytics to analyse big data, and as a prediction tool

Summarise data using a spreadsheet; collate information using spreadsheet analysis features, including charts, statistical analysis and what-if modelling; filter, group and sort data in a spreadsheet to process and display information; apply spreadsheet analysis features to develop a data dashboard

Develop a flat-file database; apply computational thinking to design a relational database with appropriate user views, including developing a data dictionary, linking tables via key fields, sorting and searching data, including using structured query language (SQL), and using forms and reports

Investigate the ethical use of data for social or enterprise research purposes; explore social, ethical and legal issues associated with using data, including bias, accuracy of the collected data, metadata, copyright and acknowledgement of source data, intellectual property and respect for ownership, including Indigenous Cultural and Intellectual Property (ICIP), permissions, rights and privacy of individuals, including cultural responsibility, and security

Investigate the influence of curated and communicated data on social behaviour, including data literacy, timeframes, signals impacting on behaviour, data swamps and educating users

Investigate the legal issues surrounding data collection and handling, including legislation, authorities responsible for data protection and data sovereignty of Aboriginal and Torres Strait Islander Peoples

Explain the purposes of data visualisation, including simplifying understanding, telling a story and highlighting significant results; describe how features of software contribute to a better understanding of datasets through data visualisation, including spreadsheets, creative design applications and combining applications to track trends and forecast; identify patterns in data by interpreting and comparing datasets for an enterprise, social or ethical issue to highlight trends and for predictive data analytics

Investigate the impact of the evolution of hardware and software on the field of data analytics, including processing power, storage/memory and communication media; describe online analytical processing (OLAP)

Assess data integrity in the development of a data visualisation, including ownership, source, validation and risk; explain the impact of enterprise data warehousing on data visualisation, including analysis and use of historical data trends and patterns, correlation with current data and data refinement/optimisation; explain how big data affects the design and development of data visualisation, including scope of visible information and types and depth of insight provided by the data

Evaluate bias in data collection, storage and analysis when developing visualisations, including accuracy, audience, data source and unconscious bias

Research, source, organise and store data appropriate for a data visualisation; design and develop a data visualisation for a specific scenario to represent trends, patterns and relationships, and illustrate predictive analysis incorporating big data; investigate and implement methods to maintain data security, including cybersecurity and data backup

Use graphic design tools to assist in the graphic development of a data visualisation; explain how user experience (UX) influences the development of effective data visualisations, including relevance to the audience, audience interpretation, customisation and live analysis; develop and implement criteria for evaluating the effectiveness of user experiences; investigate the impact of emerging hardware and software technologies on user interface (UI) and UX design and development

Evaluate the effectiveness of software tools used to develop data visualisations, including spreadsheets used to develop dashboards, presentation software used to present data analysis, business analytics services including 'as a service' products, and custom software solutions; interrogate data from a data visualisation, including interpreting what you see, aggregation, filtering, the effect of outliers and reasoning

Describe the tools and processes used to manage and document the development of an enterprise system, including problem definition, time and resource management including Gantt charts, an iterative approach, production process and technical skills, and testing and evaluation; explain the effect of the changing nature of enterprise on the development of projects, including offshore development, working remotely, freelance work and enabling the growth of start-ups

Verify and validate an enterprise computing system, including evaluating test data, trialling the operation and maintenance documentation, reviewing the impact of system implementation within relevant environments, modifying designs to improve functionality, and testing, evaluating and maintaining the developed enterprise computing system

Apply tools to inform the requirements and limitations of an enterprise system, including interviews, surveys, analytical reports, prototypes and presentations of research results; explore and apply the most suitable development approach to develop, modify and implement an enterprise system, including waterfall (structured), agile, prototyping, end-user and outsourcing

Develop an implementation plan and test its feasibility for an enterprise computing system, including design thinking, thinking and design tools including storyboards, Gantt chart and decision tree, risk analysis, hardware and software integration, training, preferred system implementation method and methodology for testing the system

Investigate tools that support the design and development of an enterprise system, including online collaboration, time/task action plans, process diary including ongoing evaluation, budget, system flowcharts, data flow diagrams and decision trees; describe how computational, design and systems thinking skills are used in the design and development of an enterprise system; select key collaborating and managing criteria appropriate to the development of an enterprise project, including designing for ease of operation and maintenance, clarifying each of the relevant informatics within the new system, outlining the role of the participants, data and components used in the system, negotiating user/client needs and wants and working collaboratively

Investigate the disruptive effects of intelligent systems, including multitasking versus digital distraction, working differently to complete the same task, automation of enterprise and manufacturing processes and impact of artificial intelligence (AI) on employment; investigate social and ethical issues to be considered when developing, implementing and using intelligent systems; investigate current and emerging technologies associated with intelligent systems, including AI; explain how intelligent systems combine innovative techniques and technologies to meet the needs of enterprise

Investigate applications of decision support systems; describe categories of decision-making within decision support systems, including an infinite set of instructions where judgement may be required (unstructured), use of a specified set of finite instructions (semi-structured) and automated decisions (structured)

Research factors that have allowed expert systems to advance from rule-based systems to integrate probability, including processing power, data availability and access, hardware costs, neural networks and webometrics; describe how people's changing needs have shaped the proliferation of expert systems, including the Internet of Things (IoT), Internet of Me (IoMe) and Industry 4.0; describe the operation of simplistic and complex intelligent agents used by search engines, including predictive search strings and the use of voice assistants to initiate interaction

Investigate common applications of expert systems, including diagnosis, monitoring, process control, and scheduling and planning; describe key features of an expert system, including knowledge base, inference engine, including forward chaining and backward chaining, and user interface (UI); compare techniques used by different inference engines, including truth maintenance, hypothetical reasoning, heuristic knowledge and fuzzy logic, and ontology classification

Investigate the hardware used in an intelligent system, including biometrics, haptics, touch and gesture, virtual and augmented reality (VR/AR), voice and sound, microcontrollers, and sensors, actuators and motors; explore how computational thinking can be integrated into the design and development of an intelligent system, including decomposition, pattern recognition, abstraction and algorithms; communicate the logical processes performed by an intelligent system by using flowcharts, data flow diagrams and infographics

Investigate the application of simulation, data modelling and the automation of systems in a range of enterprises, including education and training, business analytics and high-risk applications; explain the role of intelligent systems in surveillance, including closed circuit television (CCTV), biometric scanning, customer loyalty schemes, fraud prevention, sniffing and trolling

Investigate the infrastructure requirements for an intelligent network in an enterprise with devices linked through an Internet of Things (IoT), including networks with servers, local and cloud storage, and end-point devices, and communication links that enable the efficient control and flow of data; explore collection, type, storage, processing, application and transmission of relevant and surplus data in an intelligent IoT network; explain how AI supports efficiency in an IoT network

Verify the sources of data used in a decision support system; design and model an automated smart system using a range of inputs and outputs; implement automated processing using software; assess the output produced by a decision support system, including graphing to detail the success of decisions, and comparing the proposed versus the actual outputs

Develop a set of rules and facts that could be used within an expert system to draw conclusions; apply certainty factors to construct a decision tree for a proposed expert system; use a flowchart to develop a knowledge base of IF-THEN rules to be used by an expert system; explain how expert systems contribute to the efficiency of intelligent systems, including supercomputers, digital assistants, autonomous vehicles and streaming services