Data Science

students at Gungahlin College

Course information

Technologies enrich and impact the lives of people and societies globally. Australia needs enterprising individuals who can make discerning decisions about the development and use of technologies and who can independently and collaboratively develop solutions to complex challenges and contribute to sustainable patterns of living. Technologies play an important role in transforming, restoring and sustaining societies and natural, managed and constructed environments.

Technologies enable students to become creative and responsive designers. When students consider the ethical, legal, aesthetic and functional factors combined with the economic, environmental and social impacts of technological change, they are developing the knowledge, understanding and skills enabling them to become discerning decision-makers. Students will also be able to understand how the selection and use of technologies contributes to a sustainable and improved future. Students studying technologies will learn about the design process and its application. Students will develop research skills, computational thinking and a range of communication skills. They will refine their interpersonal and intrapersonal skills including collaboration, project management and be able to reflect on their own learning. Students will have opportunities to use design thinking and apply creativity through structured, collaborative and project based learning, solve problems, develop practical skills and apply critical thinking in the development of new ideas.

This course focuses on developing a greater understanding our world and society through data analysis, statistical inference 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 will 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.

Post-school pathways

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, in formal university qualifications and research and give students a strong grounding for future success in all areas that require competence in general Information Technology knowledge.

The courses also provides students with opportunities to develop competencies that work towards Vocational Education Certification in Information Technology that complements future studies at CIT. This includes the option of undertaking Structured Workplace Learning (SWL) or engagement in an Australian School Based Apprenticeship (ASBA).

Workload expectation

Tertiary students are expected to spend a minimum of four hours per week outside of scheduled class time on practicing the skills learned in class on a range of problems and activities. This includes both formal assessment items and preparation for tests.

Course patterns

Available as a Minor or Major, however may be combined with Digital Technology or Robotics and Mechatronics for a Major-Minor, or Double Major in Information Technology.

Organisation of Content

The following are descriptions of the individual semesters you could study in this course.

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 communicate about it 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 goal 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, and develop skills of how to use data analysis process, relevant algorithms and techniques and computational tools to analyse big data with multidisciplinary approach.

Machine Learning

This unit is a general introduction to machine learning, and statistical pattern recognition. Students will be introduced Supervised learning, and Unsupervised learning. Students will learn how to apply learning algorithms to analyse datasets from a range of sources relevant to real life to build models or applications in order to predict or have social impacts.

Data Research Project

This practical unit develops skills that students need to acquire data to learn about the world that we are in and test the hypotheses about the patterns and relationships that might otherwise be invisible; how to use computational tools to quickly analyse vast amount of data and clearly present the conclusion drawn from it and develop good and complex computational artifacts, such as written, audio, video, web and robotics to best inform and to maximise the impact.

Negotiated Study

A negotiated 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. A negotiated study unit is decided upon by a class, group(s) or individual student in consultation with the teacher and with the Principal’s approval. The program of learning for a negotiated study unit must meet all the content descriptions as appears in the unit.

Computer Requirements

Please note that the Chrome Operating System used on Chromebooks is not compatible with the specialist software used in Information Technology. It is expected that students studying Information Technology have a computer that meets the following specifications:

StreamOperating SystemCPUMemoryGraphicsPeripherals

Robotics and Mechatronics,  Data Science, and Website Design Stream

Windows, Mac, or Linux Operating System

Dual-core Intel or AMD CPU




Games Development Stream

64-bit Windows 10+ or Mac OS X10.10+

Quad-core Intel or AMD CPU – 2.5GHz or faster


Microsoft DirectX 11 compatible graphics card (GTX960m equivalent or better recommended)

Three-button mouse