Data Science is the key to solving the problems of global issues such as climate change, consumerism, energy, health and poverty through data analysis, statistical inference, predictive modelling and related methods in order to understand and analyse phenomena. Students explore and develop solutions to interesting problems in a range of contexts, forming opinions and challenging attitudes using data as evidence to form compelling and persuasive arguments for change and innovation.

Since the advent of computers, individuals and organizations increasingly process information digitally. Data processing occurs through the use of tools such as spreadsheets and databases, and progresses to more automated methods as the quantity and complexity of data being analysed increases. Cloud-based technologies have led to increasingly large data sets and big data and Machine Learning techniques now form the basis of automation in many fields of science, social science and the humanities, health and technology.

Data science is the basis of recommendation algorithms, natural language processing, computer vision, artificial intelligence in games and embedded devices, and many other modern scientific applications. Students model and implement digital solutions, manipulating, visualising and presenting data to influence decision making and predict the consequences of the actions of individuals, groups and large-scale social change.

Understanding both the power of these analytical techniques and the risks, challenges and ethical dilemmas they present, provides students with a solid foundation for further study, research and employment in a broad range of industries.

The Data Science course is written under The TECHNOLOGIES FRAMEWORK 2018: BSSS TECHNOLOGIES Framework

Achievement Standards for TECHNOLOGIES courses can be found within the Framework.

Students will demonstrate knowledge of research, skills of ideation and design, prototyping production, solution testing and communication of their understanding. Technologies promotes deep learning, creativity and innovation.

Data Representation and Analysis

This unit explores the ways that digital information is encoded, represented, manipulated, stored, compressed and transmitted. Understanding where data comes from, having intuitions about what could be learned or extracted from it, being able to use computational tools to digitally manipulate data, visualise it and identifying patterns, trends, and to use data to develop narratives and arguments are the primary skills addressed in the unit.

Big Data Analysis and Techniques

The data-rich world that we live in introduces many complex questions related to public policy, law, ethics and social impact. The goals of this unit are to develop a well-rounded and balanced view about data in the world, including the positive and negative effects of it. Students develop skills in using data analysis processes, relevant algorithms and techniques and computational tools to analyse Big Data using a multidisciplinary approach.

Machine Learning

This unit explores how Machine Learning is used to develop models for prediction, analysis, diagnosis and recommendation. Students develop an understanding of Machine Learning, and the algorithms, techniques and processes used in supervised and unsupervised models. They use Machine Learning to analyse authentic datasets from a range of sources, and investigate the inherent bias in training data. They build models or applications which enable predictions or recommendations, contextualising the social impact of their Machine Learning application.

Data Research Project

This unit enables students to undertake their own research project to develop and test hypotheses using real-world data sets. They further develop their data analysis skills, and explore patterns in data that yield interesting results. Students present conclusions drawn from their analysis, and communicate their findings through visualisations and arguments that inform and maximise impact.

Independent Study

An Independent Study unit has an important place in senior secondary courses. It is a valuable pedagogical approach that empowers students to make decisions about their own learning. An Independent Study unit can be proposed by an individual student for their own independent study and negotiated with their teacher. The program of learning for an Independent Study unit must meet the unit goals and content descriptions as they appear in the course. Students must have studied at least THREE standard 1.0 units from this course. A student can only study a maximum of one Independent study unit in each course. An Independent Study unit requires the principal’s written approval. Independent study units are only available to individual students in Year 12. Principal approval is also required for a student in Year 12 to enrol concurrently in an Independent unit and the third 1.0 unit in a course of study.

Data Science A-T-V (866 KB)

Data Science A/T/V (339 KB)