Data Science (T)

At a glance

Every recommendation you get, every trend you notice, every headline about climate or health or inequality - behind it all is data and someone who knows how to read it. In this course, students build the knowledge and skills to become that person. They’ll learn to find stories hidden in numbers and use them to make genuine arguments for change.

This course is a good choice for students who:

• want to understand why the world works the way it does, not just accept it
• like solving real problems - not textbook ones
• are curious about how data shapes sport, music, health, politics, social media - anything

About this course

Data Science puts students at the intersection of technology, ethics and real-world impact. They’ll develop skills in data analysis, statistical thinking and visual communication. They’ll ask sharp questions, find patterns, and present evidence-backed arguments that actually land. Along the way they’ll wrestle with complex questions too: Who controls data? Who gets left out? How can bias creep into algorithms and shape people's lives?

Pathways

This course can support pathways into areas such as:

• data and technology roles (data analysis, digital projects)
• science, health and social sciences where data is used to make decisions
• business and government areas that use evidence and data (planning and policy)

Units in this course

Students complete 2 units for a Minor or 4 units for a Major.

Data Representation and Analysis

Data is only powerful if you can read it and shape it into something that makes people think. This unit builds the core skills: organising messy information, running it through digital tools, and turning raw numbers into visuals that tell a clear story. Students will work with real-world data from day one.

Students will:

• use digital tools including spreadsheets and databases to interrogate and transform data
• design and build visualisations that communicate findings to real audiences
• examine how bias, data quality and interpretation choices affect the conclusions we draw

Big Data Analysis and Techniques

We generate more data every day than existed in all of human history until recently. This unit asks the question “what do we do with it”? Students work with large, complex datasets and explore the techniques that companies, governments and researchers use to find signals in the noise while wrestling with the ethical stakes at play if they get it wrong.

Students will:

• apply computational tools and algorithms to clean, filter and analyse large datasets
• investigate how big data shapes government policy, public health and social outcomes
• evaluate the risks: surveillance, bias, privacy, and the misuse of data at scale

Machine Learning

Machine learning is what powers recommendation engines, medical diagnostics, fraud detection, Large Language Models (LLMs) like ChatGPT and more. Students will build models from real data, see how they learn to make predictions, and critically examine what happens when the data they learn from is flawed.

Students will:

• train and implement models that generate predictions or recommendations from real datasets
• explore both supervised and unsupervised approaches and when each is appropriate
• investigate how designer intent and biased training data shape real-world outcomes

Data Research Project

This unit is a chance to go deep on something that actually matters to you. Students will design their own research question, source and analyse real data, test their hypotheses, and communicate findings in a way that informs and persuades a broad audience.

Students will:

• define a problem, gather data, and test predictions using rigorous analytical methods
• apply data visualisation and evidence-based argument to present conclusions with impact
• reflect on the choices made and what they reveal about the limits and potential of data

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:

* This cannot be an on-chip GPU for Windows PCs as the Game development programs are graphic intensive. M1/2 Macbook Pros can work, but will need a minimum of 16 GB of ram as it is shared between the GPU and CPU.