Applied Computing - Semester 1 Exam

Qualitative data

Data that describes qualities or characteristics, often gathered through interviews or surveys to provide insights and perspectives on a topical issue.

Quantitative data

Data that can be measured or counted numerically, such as sensor data for health monitoring, census/demographic statistics, or data collected over time.

Data generated by AI

Information created by artificial intelligence systems.

Text data

Data represented as characters or strings.

Numeric data

Data represented as integers, floating point numbers, or date/time values.

Boolean data

Data that represents a binary value, typically true or false.

Accuracy

The extent to which data is correct and free from error.

Bias

The presence of prejudice or subjectivity in data that can skew findings.

Integrity

The consistency, accuracy, and trustworthiness of data over its lifecycle.

Relevance

The degree to which data is appropriate and applicable to the task at hand.

Reliability

The consistency of data and the extent to which it can be trusted as a stable source.

Data characteristics

Qualities including size, structure, relevance, accessibility, clarity, and context.

APP 2

The Australian Privacy Principle requiring the non-identification of individuals.

APP 6

The Australian Privacy Principle requiring that information be held only for its primary purpose.

APP 11

The Australian Privacy Principle regarding security measures used to protect personal information.

APA referencing

A system for referencing primary and secondary data to acknowledge intellectual property.

Functional requirements

What the solution must actually do.

Non-functional requirements

Attributes of the solution, such as performance or usability constraints.

IPO chart

Chart used to represent the functionality of databases, spreadsheets, and visualisations.

Annotated diagrams

Visual tools used for representing the functionality and appearance of design elements.

Low-code development

An emerging trend in programming languages and AI-based code generators to speed up software creation.

One-dimensional arrays

A data structure that stores a sequence of elements of the same type.

Lists

A data structure used to hold ordered collections of items.

Records

A data structure consisting of fields with varying data types, accessible by a field index.

Mock-ups

Design tools used to represent the appearance of a software solution.

Flowcharts

Design tools used to represent the logic and sequence of a solution.

Pseudocode

A plain-language description of steps in an algorithm, used for designing code logic.

Variables

Named storage locations in memory used to initialise, access, and store data.

Control structures

Features of a programming language including sequence, selection, and iteration.

Arithmetic operators

Programming operators used for mathematical calculations.

Logical operators

Programming operators used to evaluate true/false conditions.

Conditional operators

Programming operators used to perform different actions based on whether a condition is true or false.

Hungarian notation

A naming convention where the variable name indicates its data type.

Camel casing

A naming convention where the first letter of each word except the first is capitalized

Documentation

Comments and notes within code to explain structures and aid maintenance.

Existence checking

A validation technique to ensure data is present in a field.

Type checking

A validation technique to ensure data is of the correct format.

Range checking

A validation technique to ensure data falls within specified boundaries.

Test tables

A debugging tool used to compare expected output with actual output using test data.

Relational database (RDBMS)

A system structured with tables, queries, and relationships using primary and foreign keys.

Spreadsheet structure

Data organized in rows, columns, and cells.

Data visualisation

Tools like charts and graphs used to educate, entertain, inform, or persuade.

Primary data

Data collected firsthand by the researcher for a specific purpose (e.g., surveys or interviews).

Secondary data

Data that has already been collected by someone else and is being used for a new purpose (e.g., existing reports or census data).

Divergent thinking

A thought process used to generate creative ideas by exploring many possible solutions (e.g., brainstorming or mind-mapping).

Infographic

A graphical representation of complex information or data that uses visual elements to communicate patterns and trends clearly and quickly.

Problem-solving methodology (PSM)

A systematic, four-stage approach—analysis, design, development, and evaluation—used to address challenges in Applied Computing.

Verification

The process of ensuring that data has been entered correctly (e.g., proofreading).

Primary Key

A field in a database that must be unique for every record and cannot be null, used to identify records and establish relationships.

Information

Data that has been manipulated into a meaningful and useful form to aid in decision-making.

For loop

A programming control structure used to repeat a block of code a specific, predetermined number of times.

While loop

A programming control structure that repeats a block of code as long as a specified condition remains true.

Range check

A validation technique that ensures entered data falls within a specific, acceptable upper and lower limit.

Static visualisation

A data graphic that does not change or update once it has been created and displayed.

Dynamic visualisation

A data graphic that can update in real-time or allow user interaction to filter and explore different aspects of the data.

Database

A system used for storing large amounts of structured data that requires complex relationships, data integrity, and multi-user access.

Spreadsheet

A tool best used for smaller datasets, performing quick calculations, and creating simple visualisations of data.

Problem-Solving Methodology (PSM) in order

Analysis, design, development, evaluation

Solution requirements, scope

PSM - Analysis (1)

Solution design, evaluation criteria

PSM - Design (2)

Manipulation, validation, testing, documentation

PSM - Development (3)

Strategy, report

PSM - Evaluation (4)

Solution requirements

The functional requirements (what the solution must do) and non-functional requirements (the quality attributes and constraints of the solution) defined during the Analysis stage to establish project scope and provide criteria for design and evaluation.

Scope

The defined boundaries of a project that establish what the solution will and will not include, helping to manage project expectations and prevent the inclusion of unnecessary features during the development process.

Solution design

The phase where design tools such as mock-ups, IPO charts, and flowcharts/pseudocode are used to represent the functionality and appearance of the solution.

Evaluation criteria

Specific, measurable standards established during the design phase to determine the effectiveness and efficiency of the final solution.

Manipulation

The use of software functions and techniques (such as formulas, functions, and charts) to process and transform data into information.

Validation

Techniques used to ensure data integrity and accuracy during input, such as existence, type, and range checking.

Testing

The process of checking that a software solution functions correctly, often using test tables to compare expected output with actual output.

Strategy

A plan developed to measure how well the final solution meets the original requirements and identifies any areas for future improvement.

Report

A final document that outlines the results of the testing and evaluation process, detailing the solution's performance against the initial requirements and criteria.