VCE Applied Computing - U1O1 - Data analysis Hangman

The 78 key terms

1data

Raw facts and values, like names or scores. When data is organised so it has meaning, it becomes information, like a table showing each class's average score.

2primary data vs secondary data

Primary data is data you collect yourself, like a survey. Secondary data was collected by someone else, like data from a website or database.

3Quantitative data

Numerical data that can be counted (discrete, like number of students) or measured (continuous, like temperature) and used in calculations, such as ages, scores and temperatures.

4Qualitative data

Descriptive data such as opinions, comments and experiences, often from open-ended responses or interviews. It is not measured as numbers, and is sorted into categories and descriptions.

5quantitative vs qualitative

Quantitative data answers how many or how much questions with numbers; qualitative data explains why or how people feel. Using both gives a fuller picture.

6interview

A data collection method where you ask a person open questions about an issue and record their views, giving qualitative data. It is rich in detail but a small sample may not represent everyone.

7survey

A survey collects data by asking everyone the same clear questions, such as closed or open questions on an online form, so the answers can be counted and compared.

8unbiased survey question

A survey question that is neutral, asks about one thing only, and gives answer choices that cover every honest response, so it does not push people toward a particular answer.

9sensor data

Measurements, such as heart rate or steps, that a device like a wearable records automatically at regular intervals. It is quantitative data and can reveal a person's health trends.

10census

A data collection that gathers information from every person or household in a population, not just a sample. The ABS runs the Australian census every five years.

11demographic data

Data describing characteristics of a population, such as age, location, income and education, so that different groups can be compared and analysed statistically.

12time-series data

Data recorded repeatedly over time, each value with a timestamp, usually at regular intervals, so you can see how values change and spot trends and seasonal patterns.

13Machine learning

AI learns patterns from large amounts of training data, then uses those patterns to make predictions or generate content.

14AI-generated data

New data, such as text, images or predictions, created by an AI system. Its quality depends on the data the AI was trained on, so students must verify it before use.

15open data

Datasets, such as government statistics, that anyone can download from a portal. A licence says how you may reuse them, and you must credit the source.

16string

Text made of a sequence of characters (single letters, digits or symbols), written in quotes, like 'Sam' or '0412345678'. Phone numbers are strings.

17integer

A whole number with no decimal part, positive or negative, used for counts such as the number of students in a class (e.g. 25, 0 or -3).

18float

A floating point number is a number with a decimal part, such as 1.75 or 19.95. It is used for measurements like height and for money, unlike an integer, which has no decimals.

19Date/Time

A data type that stores a calendar date and/or time of day, such as 25/03/2025 or 14:30, so values can be sorted in order and used to calculate the duration between two dates.

20Boolean

A data type with only two possible values: true or false (yes or no). Used for yes/no fields, TRUE and FALSE in a spreadsheet, and the result of a comparison such as 5>3.

21data type

Pick the data type by what you will do with the value: calculate (number), sort by date (date/time), test true or false (Boolean). Postcodes are text, not numbers, as you never do maths on them.

23dataset size

How many records (rows) and fields (columns) a dataset holds, and so how much storage it needs. Larger datasets take more space and can be slower to sort, search and process.

24structure

How data is organised. Structured data fits rows and columns with headings, like a table. Unstructured data, like free text, images or audio, has no fixed layout and is harder to analyse.

25Accessibility

How easily the people who need data can find it, open it and use it, including having the right file format, the right permissions and support for users with disability.

26Clarity

Information has clarity when it is easy to understand and not ambiguous, thanks to clear headings, labels, units and wording (e.g. 'Height (cm)' not 'Size').

27context

The background to data, such as who collected it, when, where and why, which gives the data its meaning. Without it, data can be misread.

28accuracy

Data is accurate when it is correct and free from errors, so it matches the real-world fact it records. Typos, faulty measurements and out-of-date values make data inaccurate.

29biased data

Data that unfairly favours one view or group because of how it was collected or selected, e.g. sampling bias, leading questions or missing groups.

30Integrity

Data stays accurate and unaltered unless an authorised person changes it. Accidental or deliberate changes break it, such as a tampered bank balance.

31relevant data

Data that relates directly to the question or purpose it is used for. Unneeded fields or data from the wrong population are not relevant, so they lower the quality of the information.

32reliability

Data is reliable when it comes from a trustworthy source and gives consistent results if collected again in the same way. A larger sample size also makes results more reliable.

33data quality check

Judging a dataset by testing accuracy, bias, integrity, relevance and reliability one at a time, and using evidence to show how any problem could affect conclusions.

34personal data

Information that identifies you or can be linked to you, such as your name, birthday, address, photos or school. A favourite colour on its own is not personal data.

35sensitive information

A special kind of personal information, like health, ethnic origin, religion or biometric data, that the Privacy Act protects more strictly because misuse can cause serious harm.

36Privacy Act 1988

Commonwealth legislation that sets rules for how organisations must collect, use, and store people's personal information.

37Australian Privacy Principles (APPs)

13 legal principles in the Privacy Act 1988 that set rules for how organisations collect, use, store, give access to and correct personal information. Use them as a checklist.

38APP 2

Australian Privacy Principle 2: people can choose to stay anonymous or use a pseudonym with an organisation, unless that is impractical or the law requires them to identify themselves.

39anonymise

To remove or replace names and other identifying details in data, like using ID numbers, so people cannot be recognised.

40Australian Privacy Principle 6

Requires organisations to only use or disclose personal information for the purpose it was originally collected for, unless consent or an exception applies.

41primary purpose

The reason personal information was collected. Under APP 6, check each proposed use against it. Any other use is a secondary use and needs consent or a legal exception.

42Australian Privacy Principle 11

Requires organisations to protect personal information from misuse, loss, and unauthorised access, and to destroy it when no longer needed.

43Confidentiality

Only authorised people or systems can read data. Everyone else is kept out, like student marks seen only by the student and teacher. A breach is when someone else sees them.

44permission

A rule that lets an account read, write or execute a file or resource. Read only views it, write can change or delete it, and execute runs it as a program.

45multi-factor authentication

A login step that asks for a second proof of identity, like a phone code or app prompt, as well as your password. A stolen password alone is not enough.

46Encryption

Turning a readable message into a scrambled, unreadable message using a key, so it can only be read again with decryption.

47security measures

Controls such as passwords, access control, encryption and backups that protect personal information in a dataset from misuse, loss or unauthorised access.

48APP critique

Check a system against one APP at a time: say what it does, judge if it meets that principle using evidence from the policy or audit, then suggest an improvement.

49Lack of transparency

When people are not told what data is collected, how it is used or how a conclusion was reached, such as hidden data collection, unexplained AI decisions or unclear privacy policies.

50Inaccurate or incomplete data

Wrong, missing or out-of-date data can lead to false conclusions that harm people or give an unfair picture, such as leaving out groups or using old records.

51Ownership and control of data

Who has the right to hold, change, share and delete data about a person or topic. An organisation may hold the data, but the person it describes should keep some control.

52Misuse of personal data

Using or sharing personal data in ways the person did not agree to or that harm them, such as selling it, identity theft, or using it for a different purpose.

53Data repurposing

When an AI system reuses or shares data collected for one purpose, such as training, without the knowledge or consent of the people it came from. Removing it from a trained model is hard.

54Ethical issue analysis

Name the issue, identify the stakeholders affected and the harm done, link it to an APP, then recommend an action to fix it.

55Copyright Act 1968 (Cwlth)

Australian legislation that automatically protects original creative works, like software and images, from being copied or used without permission

56copyright

The legal protection on other people's images, text and music. You need permission or a licence to use them, and you must say where they came from.

57APA referencing

A system that credits the sources of data and information using in-text citations linked to a reference list. It respects intellectual property and helps avoid plagiarism.

58(Smith, 2023)

An APA in-text citation. It puts the author's surname and the year in brackets straight after the information you used, so readers can find the full source in your reference list.

59Author. (Year). Title. Source.

An APA reference list entry gives the author, year, title and source in that order. Entries are listed A-Z by author, with a hanging indent on any lines after the first.

60Author. (Year, Month Day). Title. Site Name. URL

An APA web page reference lists the author or organisation, date, page title in italics, site name and URL. If no date, use (n.d.); with no author, start with the title. URL goes last.

61Publisher. (Year). Title [Data set]. URL

An APA reference for a dataset: publisher, (year), then the title with a [Data set] label, then the URL. For census data the publisher is the Australian Bureau of Statistics.

62OpenAI. (2024). ChatGPT (GPT-4o) [Large language model]. https://chatgpt.com

An APA reference for AI-generated content: the tool's developer as author, then year, tool name and version, a [Large language model] label and the URL.

63(J. Smith, personal communication, date)

In-text citation for your own interview. Describe your own survey in your text. Personal communications are cited in-text only and are not added to the reference list.

64The solution must

Starts a functional requirement: one thing a digital solution must do. It says what the solution does, not how, and covers one action. Example: The solution must show the score.

65Non-functional requirement

A quality describing how well a system performs, such as speed, security, or reliability, rather than what it does

66measurable non-functional requirement

A quality of a solution, such as speed, security or usability, written with a testable target, like a page loads in under 2 seconds, so you can check whether it is met.

67constraint

A limit on how a solution can be made, such as time, cost, skills, devices or software. A constraint is not a feature; it limits what you can build.

68scope

The boundary of what a solution will and will not include. Features inside the boundary are in scope. Features left out, or saved for later, are out of scope.

69requirements list

A list that sorts what a solution must do (requirements) from the limits it must work within (constraints), for one problem. Each item should be clear and testable.

70Interpreting a brief

Reading a teacher's brief to find the data, calculations and outputs needed, the constraints that limit the solution, and what is out of scope.

71IPO chart

A three-column table listing a solution's inputs (data entered), the processes applied to that data, and the outputs it produces, such as a total or report.

73annotation

A short note on a design that says what an element does or what happens when it is used, like a label on a button or an arrow showing which screen opens next.

74annotated diagram

A diagram of a database or spreadsheet with labels and notes on its parts, such as tables, fields, data types, cells, formulas and validation, explaining what each part does.

75Mock-up

A static visual representation of a solution's interface, showing layout and design before development begins

76spreadsheet mock-up

A sketch of a spreadsheet's worksheet names, header labels, data areas, formatting and result cells, drawn before building to show how it will look and where results will go.

77Data visualisation mock-up

A sketch of a chart or graph made before building it, showing the chart type, titles, labels, colours, fonts and layout so the design can be checked and improved.

78Query design

A plan for a database query that lists the table, the fields to show, the criteria used to filter records and the sort order, written before the query is built.

79Fields, Criteria, Sort

The three parts of a query design: which fields to show, which records match the criteria (using AND/OR), and how results are sorted, so the output meets the requirement.

80Interpreting a design

Reading a design's IPO chart, annotations, mock-ups and query designs together to work out what must be built, and checking it matches the requirements.