7–10Computing Technology 7–10 Syllabus (2022)

Differentiate between Loading  and Loading 

Describe the purpose of analysing data

Describe inputs, storage, transmission, processes and outputs in data analysis

Specify the functional requirements of a data analysis, including stating the purpose of a solution, describing use cases and developing test cases of inputs and expected outputs

Specify the non-functional requirements of a data analysis

Consider the social impacts and ethical and legal responsibilities in analysing data

Explore data analysis considering the perspectives of diverse groups, including Loading , culturally and linguistically diverse people, people of different ages and gender, and people with Loading 

Explain simple compression of data and types of compression

Explore the applications of small and big Loading 

Describe how data analysis has evolved in response to people's needs and opportunities

Explore design principles and issues relevant to analysing data, including Loading  principles, data trails and ownership of data

Collect and interpret data adhering to privacy and Loading  principles

Represent and store data to facilitate computation, including selecting appropriate data types, understanding data type limitations and structuring data systematically

Loading  entities, events and their attributes using structured data

Model the relationships between entities and events using relational data

Compare the usability of data using a spreadsheet or Loading  to analyse the same dataset

Investigate issues with the use of data, including Loading , security, privacy and ethics

Analyse data in both a flat-file and relational database using queries and reports

Explore how a classifier uses data analysis for machine learning

Define a real-world problem or question that can be solved by analysing data, including breaking it down into manageable parts and describing the users of the solution

Generate alternative designs and evaluate them against the requirements to select a preferred design

Develop a digital solution using a range of software to interpret and represent data to create information for a real-world scenario

Specify what data is collected, who owns it, and how it will be protected

Create interactive solutions for sharing information online with a visualisation library

Document the design and implementation of the solution in a Loading  notebook

Use appropriate methods to collect, store, validate and verify qualitative and quantitative data, considering data integrity considering privacy and personally identifying information (PII)

Summarise data using formulas, Loading  and features of a spreadsheet, including complex formulas, aggregate functions and lookup functions

Filter, group and sort data using a spreadsheet, including using filters and sorting, using conditional formatting and grouping and aggregating data

Present data and make predictions and decisions using a spreadsheet, including creating a data dashboard or report in a spreadsheet, decision formulas and optimisation

Analyse data to make decisions and generate reports using a database

Load, insert and update data in a database

Generate a data visualisation to identify trends and outliers using a range of tools

Select and use specialist terminology in context

Create a record of project development demonstrating iterative design and evaluation

Evaluate their own project and that of their peers using predetermined functional and non-functional requirements

Evaluate whether solutions meet social, ethical and legal responsibilities and cybersecurity principles

Evaluate sourced data processed using the 3Vs: volume, variety and velocity

Assess a developed solution based on calculations from datasets

Perform verification of datasets, calculations and outputs

Evaluate tools and processes used in the analysis of data for validation

Explore interests and careers in analysing data