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

Record of changes

Content

Stage 5

Enterprise information systems: Analysing data
Identifying and defining
  • 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 

Researching and planning
  • 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

Producing and implementing
  • 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

Testing and evaluating
  • 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

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