Journeys

The complete Python coding curriculum — all 63 modules from foundational syntax to advanced data structures.

Learn from foundation ideas of web app building to advance tags and elements in HTML and CSS.

VCE Applied Computing Unit 2 Outcome 1: emerging digital trends, the ideation-to-solution design process, project management and testing, and the legal/ethical frameworks that apply.

VCE Applied Computing Unit 2 Outcome 2: network hardware and security software, communications technologies, cyber security threats and controls, cryptography, and the privacy legislation that applies.

A course on learning Javascript, without HTML and CSS.

Introduction to Python coding linking to SC in Schools (https://csinschools.io/courses/introduction-to-coding-2/) Introduction to Coding course.

VCE Pseudo Code following VCAA standards for the 2026 Study Design

RADCAB and Issues in Technology - AI, privacy, security, eWaste

Victorian Curriculum Digital Technologies, Levels 5–6, Digital Systems strand: the hardware and software inside digital systems, how data moves across wired and wireless networks, and how to keep personal data and accounts secure.

Victorian Curriculum Digital Technologies, Levels 5–6, Data and Information strand: how data is represented in binary, collecting and organising data in spreadsheets, formulas and charts, and protecting personal data and privacy.

Victorian Curriculum Digital Technologies, Levels 5–6, Creating Digital Solutions strand: defining problems and requirements, designing algorithms with branching and iteration, designing user interfaces, building visual programs, and evaluating solutions.

Victorian Curriculum Digital Technologies, Levels 7–8, Digital Systems strand: choosing and justifying hardware for a task, how wired and wireless networks transmit and secure data, and protecting accounts from phishing, social engineering and malware.

Victorian Curriculum Digital Technologies, Levels 9–10, Digital Systems strand: how networked systems move and manage data, protecting confidentiality, integrity and availability with access controls and password hashing, and finding, modelling and responding to software, user and supply chain vulnerabilities.

VCE Applied Computing Unit 1 Outcome 1, Data analysis: types, characteristics and quality of data, privacy and the Australian Privacy Principles, ethics and APA referencing, requirements and constraints, and design tools for data solutions. The spreadsheet, SQL and visualisation modules are in a separate journey.

VCE Applied Computing Unit 1 Outcome 1, Data analysis: spreadsheet formulas and functions, descriptive statistics, cleaning and validating spreadsheet data, relational database design and SQL queries, charts and graphs, and data visualisation purposes, types and design conventions.

VCE Software Development Unit 3 Outcome 1, Programming: requirements and design tools, Python data types, selection and iteration, naming conventions and internal documentation, pseudocode, functions, data structures, TXT, CSV and XML data sources, validation, GUIs, object-oriented programming, searching and sorting algorithms, debugging, testing, and AI in programming.

VCE Software Development Unit 3 Outcome 2, Analysis and design: project reasons and the software development brief, Gantt charts, needs, requirements, constraints and scope, context diagrams, data flow diagrams, use case diagrams and the SRS, privacy law, file management, ideation, design tools, user experience and user interface design principles.

VCE Software Development Unit 4 Outcome 1, Development and evaluation: user-centred design, Python programming, data structures, functions and modules, classes and access modifiers, GUIs, clear code, code repositories, APIs and libraries, validation, alpha and beta testing, evaluation strategies, and assessing a project plan.

VCE Software Development Unit 4 Outcome 2, Cyber security: secure software development practices: organisational goals and in-house versus external development, vulnerabilities in insecure development environments, third-party software and code review, security controls, threat modelling, the Essential Eight and the ISM, privacy law, ethical issues, and mitigation and risk management.

VCE Data Analytics Unit 3 Outcome 1, Data analytics: requirements, constraints and scope, data types and relational databases, SQL queries and design tools, sourcing, validating and referencing data, spreadsheet formulas and data cleansing, statistical analysis, the purposes and design of data visualisations, AI trends, and testing.

VCE Data Analytics Unit 3 Outcome 2, Analysis and design: research questions and project planning, data requirements, collecting primary and secondary data, data types and structures, preparing qualitative and quantitative data, data integrity, ethical data collection, privacy, health and copyright law, APA referencing, and designing and evaluating visualisations.

VCE Data Analytics Unit 4 Outcome 1, Development and evaluation: manipulating data with software and AI tools, statistical analysis, implementing data security, developing infographics and dashboards for an audience, formats and conventions, data quality and testing, evaluation strategies, and recording and assessing project progress.

VCE Data Analytics Unit 4 Outcome 2, Cyber security: data security: why data security matters, accidental, deliberate and event-based threats, data integrity and its consequences, evaluating security with CIA, cryptography, disaster recovery plans, Australian and Victorian privacy law, the Notifiable Data Breaches scheme, emerging trends, and ethical issues in case studies.

Students learn how digital systems store text, images and sound as integers and binary, and how this affects file size and compression.

Students collect data from different sources and store, organise, clean and validate it using spreadsheets and single-table databases.

Students use spreadsheet functions, database queries and charts to analyse data, spot trends, make predictions and draw evidence-based conclusions.

Students choose and use digital tools, including unfamiliar features, to create, find and communicate trustworthy and accessible content for a diverse audience.

Students share content online responsibly and plan, manage, test and improve individual and collaborative projects through iteration.

Students investigate the digital footprint that systems and their own solutions collect, judge whether the data is essential, and learn to minimise and manage it.

Students learn to define a real-world problem for a user, break it into sub-problems, and set out its functional requirements, constraints, scope and success criteria.

Students learn to design algorithms with sequence, selection, loops and nested control structures, represent them as pseudocode and flowcharts, and trace and test them to find errors.

Students learn to design, sketch, compare, communicate, test and modify user interfaces and user experiences that suit different users.

Students learn to write, modify, test and debug Python programs that use control structures and functions, including turning their own pseudocode and flowcharts into code.

Students learn to evaluate existing and their own solutions against requirements, constraints and user feedback, and to consider their community and future impacts.

Students learn how digital data is stored and how techniques such as run-length encoding, dictionary codes and lossy or lossless formats shrink files, and they compare the results.

Students describe online documents as content, structure and presentation using HTML, CSS and Markdown, and explain why keeping these layers separate matters.

Students collect data from different sources, store and clean it in spreadsheets and relational databases, and use formulas, validation rules and SQL to manipulate it.

Students use charts, trendlines, pivot tables, interactive filters and SQL summaries to find trends and outliers, draw careful conclusions and make predictions.

Students choose emerging digital tools and advanced features to build, test and publish accessible interactive content for a diverse audience.

Students plan and manage individual and team projects with tasks, Gantt charts, Kanban boards, risk registers and iterative cycles.

Students learn about personal data, digital footprints and the Australian Privacy Principles, then audit and critique the data that systems and their own solutions collect.

Students identify stakeholders and needs, gather evidence through interviews, surveys and observation, and write a specification with functional and non-functional requirements, constraints and priorities.

Students design algorithms with logical operators, represent them as flowcharts and pseudocode, and validate them with trace tables and a range of test cases.

Students design, prototype, communicate, evaluate and modify user interfaces and user experiences, comparing alternative designs against design criteria.

Students write Python programs using input and output, variables, data types, operators, selection and loops.

Students store and organise data in lists, tuples and dictionaries and choose the structure that suits how the data will be used.

Students build modular programs from functions and imported modules and apply search and sort algorithms to tasks.

Students design and write classes, then handle errors, debug, modify and test programs with assertions.

Students evaluate existing and student-created solutions against requirements and user needs, and consider future impact, ethics and opportunities for enterprise and innovation.

Students learn to describe what a solution must do and its limits, then represent its functionality and appearance using IPO charts, flowcharts, pseudocode and mock-ups.

Students run their first Python programs and learn the characteristics of text, numeric, Boolean and date/time data types and how to choose between them.

Students work with strings, user input, type conversion, arithmetic operators, imports and date/time values in Python.

Students use sequence, selection and iteration, along with comparison and logical operators, to control how a Python program runs.

Students store and access collections of data using lists, one-dimensional arrays, records, dictionaries and tuples, and choose the right structure for a task.

Students write reusable procedures and functions with parameters and return values, and organise a program into small parts with a main function.

Students build classes and objects and apply the OOP principles of abstraction and encapsulation, including private attributes and validating setters.

Students name solution elements using conventions such as camel casing and Hungarian notation and document code with comments, docstrings and headers to support maintenance.

Students check data on entry with existence, type and range checks, using error handling, loops and reusable validation functions.

Students identify error types and check that a solution works using test tables, test data, trace tables, debugging output statements and breakpoints.

Students learn the copyright and intellectual property rules for software and explore emerging trends such as AI code generators, low-code development and improvements to readability and maintainability.

Binary, denary and hexadecimal — converting between them, adding binary, shifting bits and two's complement.

How computers store characters, sound and pictures as binary.

Measuring data, calculating file sizes, and making files smaller.

How data is broken into packets and moved between devices.

How systems check that data arrived intact.

Scrambling data so intercepting it is useless.

Inside the CPU — registers, buses and the fetch-decode-execute cycle.

How computers take data in and give results back.

Where data lives — primary, secondary, virtual and cloud storage.

The hardware and addresses that connect devices into a network.

System software, application software, and how a CPU handles interruptions.

High and low level languages, and the tools that turn code into something a computer runs.

How the web actually works, from typing a URL to seeing a page.

Digital money and the blockchain that records it.

The threats to data and systems, and the defences against them.

Systems that sense, decide and act without a person.

What makes something a robot, and where robots are used.

What AI is, how machines learn, and what expert systems do.

How a program goes from a problem to tested working code.

Breaking a big problem into parts a computer can handle.

Writing and reading algorithms before you write any code.

The standard algorithms every programmer reuses.

Storing values, naming them, working with strings and choosing the right type.

The three ways to control what a program does next.

Breaking a program into named, reusable blocks.

Storing many values under one name.

Saving data so it survives after the program closes.

Single-table databases and the SQL to query them.

Logic gates, circuits and truth tables.

The main parts inside a computer and what each one does.

How devices connect into networks to send and share data.

How computers turn pictures, colours and text into numbers — including binary.

Designing step-by-step instructions that make decisions and repeat.

Strong passphrases and safe habits when using apps and websites.

The trail you leave online, and why it lasts.

What makes one machine faster than another, and how to choose.

How data travels across wired and wireless networks, and arrives intact.

How computers store words, pictures and sound as numbers.

Why computers count in ones and zeros, and how to read them.

Collecting data and keeping it clean.

Finding trends in data and showing them clearly.

Describing objects and events as data you can search.

Designing algorithms with branching and loops inside each other.

Working out what an algorithm does, and finding where it goes wrong.

Writing, changing and fixing Python programs with decisions, loops and functions.

Writing, changing and fixing JavaScript programs with decisions, loops and functions.

How accounts and data are attacked, and how to protect them.

The trail systems leave, and whether the data was needed at all.

How hardware and software control and secure access to data across a network.

How online documents separate what they say from how they look.

Making files smaller, and what you lose when you do.

Getting data from real sources and checking it's trustworthy.

Turning data into interactive charts that support a conclusion.

Modelling how data relates, and querying it.

Designing algorithms that combine conditions, in flowcharts and pseudocode.

Proving a program works by testing it properly.

Writing Python programs in reusable pieces, including with classes and objects.

Writing JavaScript programs in reusable pieces, including with classes and objects.

How attacks work and where systems are weak.

The law on personal data, and managing your own trail.

Working out what a problem actually is before writing any code.

Designing interfaces people can actually use.

The standard techniques for turning a design into working logic.

Building, testing and refining a working program.

Identifying what data a problem needs and why.

Storing, structuring and manipulating data in code and databases.

Building and evaluating a working data solution.

How people, data and systems interact in a real solution.

Scoping a genuine problem with real constraints.

Generating, building and evaluating solution ideas.

How data moves between systems, and how it is kept safe in transit.

Scoping an exchange problem with security and privacy constraints.

Building and testing a working data exchange.

Designing a solution before coding it.

The building blocks every program is made from.

Structuring programs into reusable parts.

The algorithms every programmer should know.

How data moves across local networks and the internet.

Designing relational databases and querying them with SQL.

Keeping network communications safe.