Select a topic across a two year cycle

Recommended any combination of three topics per year.
Choose a combination of units that suits your students and context.

JAN DEC

Cycle two (Year 10)

Cycle one (Year 9)

Jan Jun Dec

Cycle two (Year 10)

Jan Jun Dec

Data science skills

Overview

This unit introduces data science as a process, focusing on specific skills used in data science. These include the acquisition of data from surveys, sensors or online repositories; storage and analysis of that data; and its visualisation, including with interactivity. When acquiring and analysing data, students can apply the Australian Privacy Principles formally introduced in the years 9–10 Cybersecurity unit. They can also build on the database and spreadsheet skills introduced in the years 7–8 Working with data unit, now querying relational databases and using more advanced spreadsheet formulas and functions.

By the end of Year 10 students acquire, interpret and model complex data with databases and represent documents as content, structure and presentation. They use advanced features of digital tools to create interactive content, and to plan, collaborate on, and manage agile projects. Students apply privacy principles to manage digital footprints.

  • explore the structure of simple relational databases and use Structured Query Language (SQL) to query them
  • apply spreadsheet formulas, functions and techniques to clean and analyse data
  • create interactive visualisations of data
  • explore the process of data science
  • acquire data from surveys, electronic sensors and online repositories
  • apply the Australian Privacy Principles to consider digital footprints and the protection of personal data.

This unit focuses on building specific skills rather than providing the opportunity for a purposeful design project. Students may choose to undertake their own data investigation in the unit Student-driven project.

It is possible to use programming languages, such as Python, to perform data analysis. Some resources in this unit may reference techniques and tools; however, they do not constitute an introduction to general-purpose programming. See the Years 9-10 Programming unit.

While Microsoft Excel is the most often mentioned spreadsheet tool in this unit, other popular software such as Google Sheets and Apple Numbers perform many of the same functions, such as formulas and charts. Consider which software suites your school makes available to students.

 

Watch this video for a quick overview of the unit and how to use its resources with your students.

Assessment

By the end of Year 10 students acquire, interpret and model complex data with databases and represent documents as content, structure and presentation. They use advanced features of digital tools to create interactive content, and to plan, collaborate on, and manage agile projects. Students apply privacy principles to manage digital footprints.

Use this rubric to assess students’ knowledge and understanding of:

  • use of relational databases to organise and structure data and perform data retrieval
  • use of spreadsheets and data visualisation.
1 (limited) 2 (basic) 3 (proficient) 4 (advanced)
Relational databases to organise and structure data and perform data retrieval with guidance, can create a basic database structure and with support uses a relevant query to retrieve data design and implement a basic database structure and perform simple queries to retrieve data creates and manages a well-structured database and efficiently retrieves data using relevant queries creates and manages a sophisticated database and efficiently retrieves data using complex queries
Spreadsheet and data visualisation with guidance can perform basic data tasks in spreadsheet software and with support creates basic visualisations of data performs basic data tasks using spreadsheet software and basic visualisations of data applies advanced functions and pivot tables independently in spreadsheet software for effective data analysis. Creates interactive visualisations with features such as buttons and dropdowns for data selection completes complex data tasks with precision using advanced functions and pivot tables. Creates sophisticated interactive visualisations, incorporating dynamic features for data selection
Data science life-cycle: defining objectives, collecting and cleaning data, performing analysis, and deriving insights with guidance, applies elements of the data science life-cycle applies aspects of the data science life-cycle. Uses basic data acquisition techniques and employs basic data cleaning methods. Uses analytical tools for simple insights applies the data science life-cycle process effectively in real-world scenarios. Demonstrates proficiency in data acquisition techniques, employs thorough data cleaning methods, and uses analytical tools to derive meaningful insights applies the data science life-cycle process to complex real-world scenarios. Uses advanced data acquisition techniques, thorough data cleaning methods, and utilises sophisticated analytical tools for in-depth insights

Unit sequence

This topic offers 3 sequential units

Accessing data in relational databases

What is this about?

Relational databases allow data to be structured in more complex and organised ways than are possible in a standard spreadsheet. By understanding the relationships between tables within a database, students can create queries (including with SQL) to retrieve only the data that is relevant to their needs. This output can be exported to a spreadsheet for further analysis.

Content description

Develop techniques to acquire, store and validate data from a range of sources using software, including spreadsheets and databases AC9TDI10P01

Analyse and visualise data interactively using a range of software, including spreadsheets and databases, to draw conclusions and make predictions by identifying trends and outliers AC9TDI10P02

Model and query entities and their relationships using structured data AC9TDI10P03

 

This sequence enables students to:

  • review the record structure for data
  • explore how databases organise data via simple relationships between tables
  • visualise the relationships using entity relationship diagrams
  • make queries on single-table and multi-table databases to select data that meets particular criteria.

Resources to include

Resources to introduce

Resources to develop and consolidate learning

Resources to extend and integrate learning

Further reading and professional learning

Analysing and visualising data with spreadsheets

What is this about?

While specialised tools exist for cleaning, analysing and visualising data, spreadsheet software remains ubiquitous due to its flexibility and accessible features. Students build on skills developed in previous years to apply more advanced functions and powerful functions like pivot tables. They explore how charts and other visualisations can be made interactive, such as by allowing buttons and dropdowns to select a different series of data.

Content description

Develop techniques to acquire, store and validate data from a range of sources using software, including spreadsheets and databases AC9TDI10P01

Analyse and visualise data interactively using a range of software, including spreadsheets and databases, to draw conclusions and make predictions by identifying trends and outliers AC9TDI10P02

Design and prototype the user experience of a digital system AC9TDI10P07

Apply the Australian Privacy Principles to critique and manage the digital footprint that existing systems and student solutions collect AC9TDI10P14

This sequence enables students to:

  • clean a spreadsheet by filtering data that is unnecessary for purpose
  • depersonalise a spreadsheet by removing data that unnecessarily contributes to a digital footprint
  • apply spreadsheet formulas and tools such as pivot tables to analyse data
  • create interactive charts to visualise data.

Resources to include

Resources to introduce

Resources to develop and consolidate learning

Resources to extend and integrate learning

Further reading and professional learning

The process of data science

What is this about?

The data science process – sometimes called the data science life cycle – captures the stages of data acquisition, cleaning and analysis with a purpose in mind. It provides a structured framework for data scientists to follow, ensuring a purpose-driven approach to extracting meaningful insights from raw data. Students can apply this framework to real-world scenarios, practising data acquisition techniques, employing data cleaning methods, and using analytical tools to derive insights. By working on hands-on projects or exercises, students can enhance their skills in systematically approaching and solving problems within the context of the data science life cycle.

Content description

Develop techniques to acquire, store and validate data from a range of sources using software, including spreadsheets and databases AC9TDI10P01

Analyse and visualise data interactively using a range of software, including spreadsheets and databases, to draw conclusions and make predictions by identifying trends and outliers AC9TDI10P02

This sequence enables students to:

  • gain an overview of the data science life cycle and what data scientists do
  • practise applying principles and stages of data science that incorporate hands-on skills with spreadsheets.

Resources to include

Resources to introduce

Resources to develop and consolidate learning

Resources to extend and integrate learning

Further reading and professional learning