WA Computer Science ATAR Year 11 — Data Management Hangman

The 110 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.

2string

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

3integer

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).

4float

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.

5Date/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.

6Boolean

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.

7data 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.

8one-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.

9grid[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.

10record

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).

11list, 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.

12CSV

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.

13TXT file

A plain text file holds unformatted characters, usually one line of text per line ended by a newline. It has no field headings or data types, so everything is read as a string.

14<student><name>Mia</name></student>

XML stores data in nested, named elements. Each element has an opening and closing tag, and child elements sit inside a root element. The tags describe the data.

15file format

The way a dataset's data is structured and stored in a file, such as CSV, XLSX, JSON or XML. Choose the format that suits the software you will open it in.

16TXT, CSV, XML

Choose a data source by structure: TXT for simple lines, CSV for tables of records, XML for nested self-describing data shared between systems. All three store data between runs.

17structure

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.

18data representation

A way of showing the same data, such as a table, list, picture, symbol or chart. You choose the one that best suits your purpose and your audience.

19database

A database gives every record the same fields and data types and lets you query large amounts of data; a spreadsheet is a flexible grid.

20relational database

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

21table, query, form, report

The four database objects: a table stores records, a query finds records, a form enters records and a report presents them.

22entity

A type of thing a database stores data about, such as a student, book or order. Each entity becomes one table, and its name is a singular noun.

23attribute

An attribute is one property of an entity, like a student's name or year level. Each attribute becomes one field (column) in the entity's table, and each instance of the entity is one record.

25VARCHAR(30)

Field size sets the maximum length or storage a value may use. VARCHAR(30) allows up to 30 characters; longer values are rejected or cut. Smaller sizes save storage.

26PRIMARY KEY

A field whose value is unique for every record in a table, such as a student ID number, so each record can be identified exactly. Names make poor keys because they can repeat.

28PRIMARY KEY (studentId, subjectId)

A composite primary key: two or more fields together identify each record uniquely. Each field can repeat alone, but the pair cannot. Used in junction tables.

29FOREIGN 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.

30one-to-many relationship

One record in a primary table matches many records in a related table. The foreign key sits on the many side, e.g. one class has many students, so class details are not repeated.

31junction table

A table that splits a many-to-many relationship into two one-to-many links by holding two foreign keys, such as Enrolment with studentId and subjectId.

32referential integrity

Every foreign key value must match an existing primary key in the related table, so no record points to something missing. The database blocks changes that break this.

33ERD

An entity relationship diagram draws each entity as a labelled box and each relationship as a line joining two boxes. Boxes list attributes, with PK and FK marked.

34crow's foot

ERD notation where a single bar at a line's end means one and a three-pronged crow's foot means many. The crow's foot end is the foreign key side of the relationship.

35ERD from a scenario

Find the entities, list their attributes, pick primary keys, then work out each relationship and draw it, with the foreign key on the many side.

36table design

Planning a table by listing a clear field name and data type for each piece of data, with one fact per field, such as FirstName and LastName as two fields.

37naming convention

A consistent rule for naming a database and its tables, such as a singular noun in lower_case with no spaces or special characters, e.g. student, applied in the same style for every table.

38student_id

A key column name in lowercase with an _id suffix. A primary key uses it in its own table, and a foreign key reuses the exact same name in a related table.

39data dictionary

A table listing each data element with its name, data type, size or format, description and validation rule, so everyone uses the data the same way when coding and testing.

40studentAge, integer, 2 digits, age in years, 15, 11 to 19

One data dictionary row for a data item: name, data type, format or size, description, example value and validation rule. Written from a requirement or IPO chart.

41table structure

The plan for one table: list each field the scenario needs, give it a data type and any validation, and choose a primary key so the table can hold every record.

42data redundancy

The same details stored again and again in one big table. It wastes space and risks typos or missed updates, like a customer's address copied onto every order.

43data anomaly

A problem caused by redundant data in one table: adding, changing or deleting a record leads to missing data, inconsistent copies or accidental loss of other facts.

44functional dependency

A field is functionally dependent on another when the other field's value determines it. The determinant fixes the value: studentId determines studentName.

48Normalise to 3NF

Take a table through 1NF, 2NF, then 3NF by removing repeating groups, then partial dependencies, then transitive dependencies, giving each new table its own key.

49SQL

Structured Query Language, the standard language for asking a database to return data (SELECT) or to add, change or remove it (INSERT, UPDATE, DELETE).

50VARCHAR(50)

A column's data type and size set in CREATE TABLE. VARCHAR(50) holds text up to 50 characters. Other types: INT, DECIMAL(8,2), DATE, BOOLEAN. They control what a column stores.

51CREATE TABLE

SQL command that builds a new table by naming its fields and their data types, such as a PRIMARY KEY. INSERT INTO ... VALUES then adds one record to that table.

52FOREIGN KEY (field) REFERENCES other_table(field)

Used in CREATE TABLE to link a column to another table's key. The database then only accepts values that match an existing key, so bad links are rejected.

53CHECK (age BETWEEN 0 AND 120)

A constraint is a rule on a column. This CHECK rejects any record with an age outside 0 to 120. NOT NULL (must have a value) and UNIQUE (no repeats) are constraints too.

54validation

An automatic check that data is reasonable and allowed when it is entered, such as being in range or the right type, so typos and impossible values are caught.

55validation rule

A rule set on a database field that checks each entry, such as a number range, a maximum text length or a drop-down list, and rejects any record that breaks the rule.

56Import CSV

Fills a new database table from a CSV file. The first row becomes the field names, and you choose a data type for each field, such as text or number, and check them after the import.

57SELECT * FROM students;

Type an SQL statement in the tool's query window, run it, then read the results table. If an error shows, read the message and fix the statement.

58UPDATE table SET field = value WHERE condition;

UPDATE changes field values and DELETE FROM removes records. Always add WHERE so only the chosen records change. Without it, every record in the table is changed or deleted.

59SELECT 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.

60SELECT lastName, firstName FROM students;

The output has only the fields named, as columns in the order written after SELECT, with a heading row of field names and one row per record kept (all records if there is no WHERE).

61WHERE

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.

62WHERE Town = 'Leeds'

The value in a WHERE condition must match the field's data type: text and characters go in quotes, numbers do not, and Boolean fields use TRUE or FALSE, e.g. Member = TRUE.

63AND, OR, NOT

Combine WHERE conditions: AND needs both true, OR needs at least one true, NOT reverses a condition. Brackets set the order and decide which rows are returned.

64WHERE (A OR B) AND C

Brackets make SQL work out the OR first, then the AND. A record must meet C and also A or B. Moving the brackets, as in A OR (B AND C), can return different records.

65BETWEEN low AND high

Tests if a value is inside a range, including both limits. Its partner IN (a, b, c) checks a value against a list, replacing repeated OR. NOT IN excludes the list.

66LIKE '%at%'

LIKE in a WHERE clause matches part of a text value. The % wildcard stands for any characters, so '%at%' finds text containing at, 'S%' starts with S and '%s' ends with s.

67IS NULL

NULL marks a missing value. Test for it with IS NULL or IS NOT NULL, because = NULL never matches. NULL is not a blank or 0, and aggregates ignore it.

68ORDER 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.

70SELECT FROM WHERE ORDER BY

The order of clauses in a full SQL query: SELECT, FROM, WHERE, then ORDER BY. WHERE filters the rows first, then ORDER BY sorts the rows that are left.

71SELECT 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.

72SELECT COUNT(*) FROM Member WHERE Town = 'Leeds';

WHERE filters the records first, then COUNT or SUM works only on the matching ones. This counts members from Leeds. If no record matches, COUNT gives 0.

73GROUP 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).

74JOIN

A join matches each foreign key value in one table to the primary key value in another, so rows from both tables show together. Rows with no match are left out.

75ON orders.customer_id = customers.customer_id

The join condition: it matches the foreign key in one table to the primary key in the other. The = sign compares the two values to find matching rows. It does not assign a value.

76table.field

Names the table a field comes from, like students.name. Needed in a join when both tables share a field name, or SQL gives an ambiguous column error.

77FROM students s

Gives the students table the short name s after FROM, so the rest of the query can write s.firstName instead of students.firstName.

78JOIN ... ON ... WHERE ... ORDER BY

After JOIN ... ON links two tables, WHERE filters the joined rows and ORDER BY sorts them. Clause order: SELECT, FROM, JOIN ON, WHERE, ORDER BY.

79JOIN subjects ON results.subjectId = subjects.subjectId

A second JOIN line chained after the first to reach a third table. It follows a foreign key in the junction table to the primary key of the next table.

80Cartesian product

A join with no ON condition pairs every row of one table with every row of the other, so 3 customers and 4 orders give 12 rows, most of them wrong.

81multi-table query

A query that gets fields from more than one table: find which tables hold them, JOIN them on matching keys, then add WHERE and ORDER BY to match the question.

82SELECT fields FROM table WHERE condition ORDER BY field DESC;

Turn a request into one SQL statement: pick fields after SELECT, the table after FROM, conditions in WHERE, sort with ORDER BY, and use SUM or COUNT for totals and how many.

83SELECT WHERE ORDER BY

Read a query one clause at a time: SELECT names which fields are shown, WHERE picks which records, and ORDER BY sets the order the results appear in.

84WHERE, ORDER BY, SELECT

To find a query's output, first keep the rows that pass WHERE, then sort them with ORDER BY, then show only the SELECT fields or the SUM or COUNT result.

85SQL error

A mistake in an SQL script, such as text without quotes, > instead of >=, AND instead of OR, clauses in the wrong order, a missing table after FROM or a missing semicolon.

86SELECT field FROM table WHERE condition;

Completing an SQL script: read the task, then fill the gap with the right field, WHERE condition, ASC or DESC, AND or OR, or SUM or COUNT so the query returns exactly what is asked.

87Test a query

Predict the rows a query should return from a small sample, run it, then compare the actual result with your prediction. Check row counts, boundary values and JOIN matches.

88Export query results as CSV

Save the rows a query returns as a CSV file with a header row of field names. Import it into a spreadsheet, then check the columns came across correctly before further analysis.

89personal 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.

90sensitive 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.

91Privacy Act 1988

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

92APP entity

An organisation or government agency that must follow the Australian Privacy Principles under the Privacy Act 1988, such as federal agencies, health providers and larger businesses.

93APP lifecycle groups

The 13 APPs grouped by what happens to data: openness (1-2), collection (3-5), use and disclosure (6-9), quality and security (10-11), access and correction (12-13).

94consent

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.

95APP 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.

96APP 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.

97Australian 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.

98APP 8

Before sending personal information overseas, such as to a cloud server, an organisation must take reasonable steps to make sure the overseas recipient will also protect it.

99APP 10

Australian Privacy Principle 10: take reasonable steps to make sure the personal information you collect, use or disclose is accurate, up to date and complete.

100Australian Privacy Principle 11

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

101APP 12 and APP 13

Rights to ask an organisation for access to the personal information it holds about you (APP 12) and to have it corrected if it is wrong or out of date (APP 13).

102OAIC

The Office of the Australian Information Commissioner: the government body that oversees the Privacy Act, gives guidance, handles privacy complaints and can act on APP breaches.

103data breach

When data a system holds is stolen or leaked. Data you never collect cannot be breached, so the more you keep, the bigger the risk, including identity theft and misuse.

104data retention

Keeping data only as long as its purpose needs it, then deleting it, including old accounts, finished project data and backup copies.

105security measures

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

106permission

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.

107Backup strategy

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

108disposal

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.

110Privacy Amendment (Notifiable Data Breaches) Act 2017

Amends the Privacy Act so covered organisations must tell the OAIC and affected people after an eligible data breach, so people can protect themselves.

111NDB scheme steps

Check if a breach is eligible (serious harm likely), then notify the OAIC and affected people as soon as practicable. Failing to notify risks penalties under the Privacy Act.

112anonymise

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

113Misuse 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.

114Ownership 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.

115Inaccurate 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.

116Indigenous data sovereignty

The right of Aboriginal and Torres Strait Islander peoples to own, control and decide how data about their communities is collected, used and shared, with consent and respect.

117Ethical 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.