VCE Data Analytics - U3O2 - Analysis and Design Hangman

The 151 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.

3research question

One question that a data project sets out to answer using collected data. It is narrower than a topic and must be answerable with data the project can collect.

4Clarity

A research question feature: it is specific, uses unambiguous key terms and asks about one thing only, so everyone reads it the same way.

5measurable research question

A research question is measurable when it can be answered with data that is counted, measured or coded, using clear variables, units or categories, not opinion words.

6feasible

A feasible research question can be answered with the time, skills, software and access to data that you actually have available.

7originality

A research question feature: it explores something new or from a fresh angle, such as a new group, place or time period, after checking existing work, instead of copying others.

8Refine a research question

Test a weak question against clarity, measurability, feasibility and originality, then rewrite it: make vague words specific and narrow it down.

9Gantt chart

A project management tool that shows tasks and their timeframes as horizontal bars along a timeline

10task breakdown

Splitting a whole project into small tasks, each with a clear outcome, a time estimate and an order (dependencies), so you can track progress as the work gets done.

11Sequencing

Putting tasks in the order they must happen on a Gantt chart. Tasks that depend on others start after them, while independent tasks can run in parallel (at the same time).

12time allocation

The estimate of how long each task will take, shown on a Gantt chart as the length of its bar. Bars are measured in days or weeks and together set the total project length.

13dependency

A link between two tasks where one cannot start or finish until the other is complete. The first is the predecessor, the next is the successor. Shown on a Gantt chart by an arrow.

14milestone

A marked checkpoint in a project plan, such as a finished prototype, showing a major piece of work is complete by its deadline. Used to check progress.

15critical path

The longest chain of dependent tasks in a project. It sets the shortest possible finish time, and any delay to a task on it delays the whole project. Tasks off it have float.

17project log

A record of each work session: the date, what was done, any problems and the next step. It shows actual progress against the plan and evidences why the plan changed.

18Progress shading

Shading the completed part of each task bar on a Gantt chart, then comparing it with the planned dates to see whether the task is on time or late.

19The 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.

20Non-functional requirement

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

21measurable 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.

22data requirements

A list of the data items, types, sources and amount needed to answer a research question, such as the variables, primary or secondary source, time period and sample size.

23constraint

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.

24scope

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.

25requirements 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.

26survey

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.

27unbiased 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.

28interview

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.

29good interview question

A question that is open, neutral and about the user's real task, so it draws out detail instead of a yes or no. Ask 'How do you...?' then follow up with 'Why?' Don't lead them to an answer.

30observing

Watching a user do a task to see where they get stuck, without helping or interrupting. You write down what you see instead of just asking them what they think.

31observation sheet

A planned table for recording what, when and how often a behaviour is seen, using tally counts, a time and date column, and facts rather than opinions.

32search operator

A special symbol or word, such as quotation marks, a minus sign, site: or filetype:, that you add to your keywords to narrow a web search to the results you want.

33RADCAB

A checklist of six questions (Relevancy, Appropriateness, Detail, Currency, Authority, Bias) used to check if information is trustworthy.

34open 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.

35search filter

A setting in a data repository, such as topic, date range or file format, that narrows the results so you find and download only the data you need. Check the metadata first.

36relational database

A database that stores data in separate tables of fields (columns) and records (rows), with tables linked by shared values such as keys.

37SELECT name, age FROM students;

SELECT lists the fields to show and FROM names the table. This returns the name and age columns for every record in students. SELECT * shows all columns. End with a semicolon.

38WHERE

An SQL clause that keeps only the rows meeting a condition, such as WHERE age >= 15 or WHERE town = 'Perth'. Text goes in quotes, and AND or OR can combine conditions.

39ORDER BY

Sorts the rows a SELECT query returns by a chosen field. It sorts A to Z or lowest to highest by default (ASC), or the reverse if you add DESC after the field name.

40SELECT AVG(score) FROM results;

An aggregate function works out one value from many rows. COUNT, SUM, AVG, MIN and MAX give the count, total, mean, lowest and highest. This query returns the mean score.

41GROUP BY

An SQL clause that sorts rows into groups sharing the same field value, then applies an aggregate like AVG or COUNT to each group, giving one result row per group (e.g. average score per class).

42FOREIGN KEY

A field in one table that holds the primary key value of a record in another table, linking the two tables together so related data is stored once and not repeated.

43INNER JOIN t2 ON t1.key = t2.key

Combines rows from two tables where a foreign key matches a primary key, so related data shows in one result. Use table.field names to say which table each field belongs to.

44string

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

45integer

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).

46float

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.

47Date/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.

48Boolean

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.

49data 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.

50structure

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.

51data table

A grid that organises data. Each column has one heading, and each row holds one record, like one student or one pet. There are no blank rows.

52one-dimensional array

A single row of items of the same data type, stored under one name. Each item is found by its index number, starting at 0. In Python, a list is used as an array.

53record

A structure that groups related fields of different data types to describe one thing, such as a student's name (text), age (integer) and mark (decimal).

54grid[row][col]

A 2D array stores data in rows and columns like a table. Use the row index first, then the column index, to reach one element, such as a seat in a seating plan.

55list, dict, tuple

Pick the structure by how you use the data: a list for an ordered collection, a dict for lookup by key, and a tuple for a fixed group of values that should not change.

56CSV

A text file with one record per line, fields split by a delimiter such as a comma, and an optional header line of field names. Text containing commas goes in quotes.

57CSV file

A plain text file that stores table data with commas between the values. A spreadsheet can open it and show the data in rows and columns.

58Quantitative 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.

59Qualitative 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.

60quantitative 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.

61Suitability for visualisation

Quantitative data can be charted directly as numbers. Qualitative data (text) must first be coded or summarised into counts or categories before it can be charted.

62code

A short label or number given to each qualitative response, such as 1 = positive comment, so that similar responses can be grouped, counted and compared.

63coding frame

A table of categories, each with a definition and example, so every response gets one consistent code. Categories do not overlap, and an Other category catches the rest.

64frequency

The number of responses given a particular code. Counting each code turns qualitative data into counts you can chart; dividing by the total gives a percentage.

65=COUNTIF(range,A2)

Used in a frequency table: list each category in column A, then put this beside it to count how many times that category appears in the data range. Copy it down for every category.

66clean data

Finding and fixing mistakes in data, like typos, spelling variations, duplicate rows, missing values and impossible values, so the results are correct.

67Sort

Sorting puts the rows of a table in order using the values in one column, like A to Z or smallest to largest, and keeps each row together.

68filter

A filter shows only the rows that match a value you choose and hides the other rows. The hidden rows are not deleted. Clear the filter to see them all again.

69pattern, trend, outlier

Look for patterns and trends in data, like highest, lowest, going up or down. An outlier is a value that stands out from the rest. Compare groups to see differences.

70scatter graph

A chart that plots each record as a point using two number columns, one on the x axis and one on the y axis, to show whether two things are related.

71correlation is not causation

Two things can rise or fall together without one causing the other. A trend shows a relationship, not the reason, so a hidden third factor may explain both.

72Integrity

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

73accuracy

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.

74authenticity

Data is authentic when it is genuine and really comes from the source it claims. Check the source and watch for fake or altered data.

75correctness

Each value is entered in the right field and right form, and processing gives the right result. It differs from accuracy, which is how close a value is to the real world.

76reasonableness

Data is reasonable when its values are sensible and plausible for what they describe. An age of 250 fails a reasonableness check on a range, such as 0 to 120.

77relevant 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.

78timeliness

Data is timely when it is up to date enough for its purpose, judged by its collection date or date of last update. Old census figures may be too out of date.

79data 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.

80personal 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.

81sensitive 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.

82consent

Asking permission before collecting someone's personal data and explaining how it will be used. The person can say no. For young people, a parent or guardian may also need to agree.

83participant information statement

A document telling participants the study's purpose, what they will do, how data is used and stored, and that taking part is voluntary with a right to withdraw. It lists contact details.

84Consent form

A document where a participant ticks each use of their data, then signs and dates to agree to take part after reading the information statement. A guardian signs if the participant is a minor.

85data minimisation

Only collect the personal data you need for your purpose. Less data means less risk, so skip unneeded questions, store data safely and delete it when finished.

86anonymise

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

87participant code

A code such as P01 used instead of a name. Names and indirect identifiers like school or suburb are removed, and the key list linking codes to people is stored separately.

88Copyright Act 1968 (Cwlth)

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

89Privacy Act 1988

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

90Australian 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.

91APP 3

Australian Privacy Principle 3: only collect personal information that is reasonably necessary for your functions, and collect it lawfully and fairly. Take extra care with sensitive information.

92APP 4 unsolicited personal information

If an organisation gets personal information it did not ask for, it must decide if it could have collected it. If not, it must destroy or de-identify it.

93APP 5 notification

APP 5 says an organisation must tell people, at or before it collects their info, who is collecting it, why it is needed, and who it may be shared with.

94Australian 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.

95Australian Privacy Principle 11

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

96Health Records Act 2001

Victorian legislation that sets rules for how health information must be collected and kept secure by public and private providers.

97HPP 1

Health Privacy Principle 1: only collect health information that is necessary, collect it lawfully and fairly, and tell the person why you are collecting it.

98Health Privacy Principle 2

Requires health information to be used or disclosed only for the purpose it was originally collected for, unless consent or an exception applies.

99HPP 4

Health Privacy Principle 4: health information must be kept secure from misuse and loss, and not kept longer than needed. Destroy or de-identify it safely when the retention period ends.

100Privacy and Data Protection Act 2014

Victorian legislation that sets rules for how the Victorian public sector must collect and securely protect personal information.

101IPP 1

Information Privacy Principle 1 (Vic): only collect personal information that is necessary for your functions, collect it lawfully and fairly, and tell people why you are collecting it.

102Information Privacy Principle 2

Requires personal information to be used or shared only for the purpose it was originally collected for, unless consent or an exception applies.

103Information Privacy Principle 4

Requires organisations to take reasonable steps to protect personal information from misuse, loss, unauthorised access, and unwanted modification.

104IPP 10: sensitive information

Victorian privacy principle: sensitive information (health, ethnic origin, beliefs) can only be collected with the person's consent or when the law allows it.

105principle, met or not, evidence

Check a scenario against each relevant principle: name the principle, state whether it is met or breached, and quote evidence from the scenario. Then recommend a fix.

106copyright

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.

107APA 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.

108(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.

109Author. (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.

110Author. (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.

111Publisher. (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.

112archive

Moving data no longer in everyday use to long-term storage so it is kept safely for a set retention period and can be retrieved later. It is not the same as a backup copy.

113Backup strategy

A plan for how and when backups are taken and stored, such as scheduling regular backups and keeping copies in multiple locations.

114full backup

A backup that copies all selected files every time it runs. Restoring is simple, but it uses lots of storage and takes longer than other backup types.

115incremental backup

A backup that copies only files changed since the last backup of any type. It is small and fast, but a restore needs the full backup plus every increment.

116differential backup

Copies all files changed since the last full backup. It grows each day, and a restore needs the full backup plus only the latest differential.

117full vs incremental vs differential

They differ in size, time and restore steps: full copies all data, incremental saves changes since the last backup, differential saves changes since the last full backup.

118disposal

Permanently destroying files and storage media no longer needed (e.g. secure deletion, shredding old drives) so data cannot be recovered, once any retention period ends.

119Ideation technique

A structured method used to generate a wide range of possible solutions to a problem before one is selected for development

120brainstorming

Listing as many ideas as possible quickly, with no early criticism and quantity first, then reviewing the ideas afterwards and grouping them to find the best ones.

121mind map

A diagram with a central topic in the middle and branches and sub-branches that link related ideas, used to generate and organise ideas during design.

122mood board

A collage of colours, images, fonts and styles that shows the look and feel wanted for a design, helping a team agree on a visual direction before building the interface.

123screen sketch

A simple drawing of a screen. It has an outline, then a box or shape for each button, text box or label, drawn where it will go. You draw one sketch for each screen.

124annotation

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.

125dynamic data visualisation

A data display the viewer can change with filters or slicers, unlike a fixed infographic, so each user can explore the data and answer their own questions.

126dashboard

A single screen that combines charts, tables and key figures so a viewer can monitor data at a glance, such as sales or attendance.

127alignment

A UI design principle that lines up elements along common edges or centres, such as a left edge or grid, so the layout looks tidy and is easy to scan.

128balance

The even spread of visual weight across a screen so no side feels too heavy. It can be symmetrical (mirrored) or asymmetrical (different elements that still feel equal in weight).

129contrast

The difference in colour, size or brightness that makes key elements and text stand out from their background so they are easy to read and notice.

130icon

A small simple picture that makes a topic quick to recognise. Keep icons relevant and in one style, use them only where they help the data, and credit or get permission for any you borrow.

131space

The empty area around and between interface elements, used to group related items, separate unrelated ones and keep a layout uncluttered and easy to read.

132text formatting

Using font, size, weight and colour consistently so text is easy to read and shows importance, such as larger bold headings above smaller body text.

133table

A visualisation component that shows exact values in rows and columns, used when the audience needs the precise numbers. Clear headers and only the rows that matter keep it readable.

134usability

How easily, quickly and accurately users can learn and use software to achieve their goals, including ease of learning, efficiency of use, and error prevention and recovery.

135user flow

The path of screens a user follows to finish a task, like logging in or buying a ticket. Navigation controls such as back and home buttons help them move along it in as few steps as possible.

136interactivity

Lets viewers act on a visualisation, such as filtering, selecting or hovering, and the response must be clear and quick, e.g. a hover tooltip showing details.

137slicer

A set of buttons linked to a table, pivot table or chart. Click a button to filter the data, so a viewer can explore it interactively. Clear the filter to show everything again.

138Annotated design review

Checking a mock-up against each UI principle in turn (alignment, balance, contrast, space, text), annotating the improvements needed, then revising the design.

139design criteria

Checkable standards, such as easy to read, easy to navigate or meets the requirements, that an interface is designed to meet and then judged against.

140evaluation criteria

Statements written from the research question, audience and requirements, worded so each visualisation design idea can be rated with evidence.

141decision matrix

A table that scores each design idea against each criterion, then totals the scores (weighting key criteria) so the best design can be chosen using evidence.

142efficiency

How well a solution uses resources such as time, processing, memory, storage and user effort to do its job. An efficient solution is fast, light on memory and storage, and saves the user time.

143Effectiveness

How well a solution does what it was meant to do, giving correct, complete and usable results that meet the requirements and suit its purpose.

144effectiveness and efficiency

Effective = viewers answer the research question correctly. Efficient = they answer quickly, with little effort. Back each judgement with evidence, like test times.

145Storyboard

A sequence of visual panels showing how a user moves through and interacts with a solution, step by step

146storyboard panel

One frame of a storyboard showing the visualisation before and after a user action, with a note of the action (such as a filter click) that causes the change. Arrows link the panels.

147Mock-up

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

148Data 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.

149spreadsheet 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.

150IPO 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.

152Query 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.

153Fields, 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.