ACARA Digital Technologies Years 9–10 — Acquiring and Validating Data Hangman

The 119 key terms

1data science

Using data to answer questions and make decisions by following a purposeful process, instead of guessing. For example, using sport or health data to find real answers.

2data science life cycle

A repeating cycle of five stages: define the objective, acquire data, clean it, analyse it, communicate insights. Findings can send you back to an earlier stage.

3data science roles

Data engineers prepare data, data analysts explore it and share results, and data scientists build models and insights. Together they share the data science life cycle.

4research 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.

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.

6data 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.

7data

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.

8primary 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.

9Quantitative 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.

10Qualitative 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.

11quantitative 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.

12string

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

13integer

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).

14float

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.

15Date/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.

16Boolean

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.

17data 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.

18survey

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.

19unbiased 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.

20sample

The part of a population you collect data from. It must represent the whole population, so use a big enough size, choose randomly and avoid sampling bias.

21pilot test

Trying a survey on a few people first to find confusing questions, missing options and timing problems before the real data collection begins.

22response validation

A rule set on an online form question, such as required, a number range or multiple choice instead of free text, that blocks bad answers so only valid data is collected.

23Form responses sheet

A spreadsheet linked to an online form. Each submitted response becomes a new row, each question is a column, and a timestamp column records when it was sent, giving data ready to use.

24code

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.

25=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.

26sensor

An input device that measures a physical quantity, such as heat or light, from the environment and sends the readings to a computer or microprocessor.

27ADC (analogue-to-digital converter)

Turns the continuously varying analogue readings from a sensor into digital values (binary numbers) so a computer can process them.

28data first, then sensor

Each sensor captures one type of data, so identify the data first (e.g. moisture in soil, humidity in air), then name the sensor that measures it.

29temperature sensor

An input device that measures how hot or cold something is. Used in central heating, ovens, greenhouses and weather stations.

30light sensor

An input device that measures how bright the light is and sends the light level to a computer. Used in automatic street lights, screen brightness and greenhouses.

31humidity sensor

An input device that measures the amount of water vapour (moisture) in the air. Used in weather stations, greenhouses and air conditioning systems.

32moisture sensor

An input device that measures the amount of water in a solid material such as soil. Used in automatic plant watering and farming. Not the same as a humidity sensor, which measures air.

33sensor 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.

34time-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.

35logging interval

How often a sensor records a reading. Short intervals give more detail but bigger files; long intervals save space but can miss fast changes.

36one row per reading

A sensor log stores each reading as its own row: a timestamp column for the date and time, then one column per measurement, with units in the header, such as Temp (C).

37sensor error

A reading that is wrong due to a faulty or uncalibrated sensor or electrical noise, so check for stuck values, impossible spikes and offsets against a known value.

38open 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.

39search 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.

40authentic data

Real data collected by a trusted organisation and kept in a large repository; check its publisher, date and relevance to your question.

41file 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.

42CSV

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.

43CSV 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.

44copyright

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.

45personal 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.

46consent

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.

47data 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.

48anonymise

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

49Ownership 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.

50Indigenous 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.

51participant 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.

52spreadsheet

A grid of columns (letters, running down) and rows (numbers, running across). A cell is where a column and row meet, like B3. Each worksheet holds one grid.

53Click a cell, type, press Enter

To store data, click a cell, type your value, then press Enter to save it. Tab saves it and moves right. Text lines up left and numbers line up right.

54header label

A label in the first row or column that names the data below or beside it. Use one per column, add units like Height_cm, and keep capitalisation consistent.

55data 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.

56raw data copy

Keep the original data untouched on its own Raw sheet or file, then clean a copy on a Clean sheet, so any mistake can be undone. Version numbers like data_v2 and backups help.

57database table

A database table stores data in columns called fields and rows called records. A single-table database keeps all its data in this one table.

59PRIMARY 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.

60relational database

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

61FOREIGN 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.

62CREATE 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.

63Import 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.

64validation vs verification

Validation checks entered data is reasonable and complete. Verification checks it matches the source data, for example by proofreading.

65validation

An automatic check that data is reasonable and in the right form when entered. It blocks bad data early, but it cannot prove the data is true (a valid age of 15 could still be wrong).

66validation check

A test that an entry is sensible. Types include presence, data type, range, length, format and list. Each one tests a different property of the entry.

67Data Validation

A spreadsheet tool that limits cells to allowed entries, like a whole number from 1 to 100 or a drop-down list, and shows an error message if someone types anything else.

68Data > Data validation > Whole number between

A rule that only lets a cell accept a number between a minimum and maximum, like age 11 to 14. Numbers outside the range are rejected. Use Decimal for numbers with parts.

69List validation

A rule that lets a cell accept only items from a set list, shown as a drop-down. Items can be typed in or taken from a range of cells, which keeps spelling consistent.

70Error alert

A message that pops up when someone enters data that breaks a validation rule. It can Stop the entry, Warn about it, or give Information, and a good message explains how to fix it.

71ISBLANK

A spreadsheet function that checks a cell. It returns TRUE if the cell is empty and FALSE if it holds data, so it works as an existence check. COUNTBLANK counts empty cells in a range.

72ISNUMBER

A function that tests whether a cell value is a number. It returns TRUE for a number and FALSE for text, so it works as a type check. ISTEXT does the same job for text.

73=AND(B2>=0,B2<=100)

A range check formula. It returns TRUE only if the value in B2 is at least the minimum (0) and at most the maximum (100), so boundary values pass and anything outside is rejected.

74=LEN(A2)=4

LEN counts the characters in a cell, spaces included. Comparing it to a number, like =LEN(A2)=4 for a 4-digit postcode, gives TRUE if the length is right and FALSE if not.

75format check

A validation check that tests a value follows an expected pattern, like two letters then three digits or a real date. A correct pattern does not prove the data is right.

76=IF(test, value_if_true, value_if_false)

Checks a test, like B2>=50. Shows the second value if the test is true and the third if it is false. Text results go in quotes, e.g. =IF(B2>=50,"Pass","Fail").

77=IF(AND(B2>=0,B2<=120),"OK","Check")

Shows OK if B2 is between 0 and 120, otherwise Check. Copy it down the column beside the data, then filter on Check to find the invalid entries.

78double entry check

Data is entered twice and the two entries are compared. Any difference shows a mistake, so the user must enter it again. It misses the same mistake made twice.

79validation 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.

80CHECK (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.

81Authenticity

Data is authentic when it genuinely comes from the source it claims and has not been forged or spoofed. Verifying its origin shows it can be trusted.

82accuracy

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.

83Correctness

Data is correct when it is in the right form and follows set rules, such as the right data type, format, valid values and calculation. It is not the same as accuracy.

84reasonableness

Data is reasonable when its value is sensible and believable in its context, so impossible values or odd outliers (like an age of 250) are flagged by reasonableness checks.

85relevant 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.

86timeliness

Data is timely when it is up to date and available when needed. Out-of-date records or delayed updates can lead to poor time-critical decisions.

87biased 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.

88reliability

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.

89data integrity

How trustworthy data is. Test it against six characteristics, one at a time: accuracy, authenticity, correctness, reasonableness, relevance and timeliness. Failing any one means it can't be trusted.

90data 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.

91Inaccurate 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.

92clean data

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

93Sort

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.

94filter

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.

95=TRIM(A2)

A text function that returns a tidy copy of the text in A2 with extra spaces removed. UPPER, LOWER and PROPER work the same way to fix capitals, e.g. =PROPER(A2) gives Mia Chen.

96Find and Replace

A spreadsheet tool that swaps every spelling variation of a category, such as Vic or VIC, for one agreed value like Victoria, so each category is counted only once.

97Remove Duplicates

A spreadsheet tool that deletes rows repeating the same values in the columns you choose to compare, keeping the first match. Always keep a copy of the raw data first.

98=COUNTIF(range, criteria)

Counts how many cells in a range match a value or condition, such as "Yes" or ">10". Example: =COUNTIF(C2:C30,"Yes") counts every Yes in C2 to C30.

100=COUNTIF(A$2:A$100,A2)

Counts how many times the value in A2 appears in the whole column range. A result above 1 flags a duplicate, so review those rows before deleting any.

101missing value

An empty cell where data should be. Find it with a filter for blanks, then delete the row, fill it from a reliable source or exclude it so AVERAGE and COUNT are not distorted.

102=VALUE(A2)

Converts text that looks like a number, such as '45' stored as text, into a true number so it can be added, averaged and sorted correctly. Left-aligned numbers are a warning sign.

103=LEFT(A2,3)

Text functions split text or join it. LEFT, RIGHT and MID take part of a value; CONCAT or & joins parts into one. This formula returns the first 3 characters of cell A2.

104Text to Columns

A spreadsheet tool that splits the text in one column into separate columns at a chosen delimiter, such as a comma or space, e.g. a full name into first and last name.

105error value

A message starting with # (such as #DIV/0!, #N/A, #VALUE! or #REF!) that a cell shows when a formula fails. It hints at what went wrong so you can fix the formula or data.

106=MIN(range) and =MAX(range)

MIN finds the smallest (lowest) value in a range of cells and MAX finds the largest (highest) value. For example, =MAX(B2:B10) shows the top score in cells B2 to B10.

107Conditional formatting

A spreadsheet tool that changes a cell's colour or icon automatically when its value meets a rule, so highs, lows and outliers stand out.

108outlier

A value far from the rest. Check if it is a data entry error or a real extreme value, then correct, remove or keep it, and record your decision.

109cleaning log

A record of each change made to the data: what changed, how many rows it affected and why. It lets other people check the cleaning and repeat it.

110expected result

The answer you work out by hand using a few rows of data. Compare it with the formula's actual result. If they match, the formula works. If not, check the formula and its range.

111End-to-end integrity checking

Following data through every stage, from collection to chart, and confirming at each stage that it is unchanged and correct, for example by matching record counts and totals.

112=SUM(B2:B10)

A formula that adds up all the numbers in a range of cells. The colon means 'from B2 to B10'. The AutoSum button can type it for you.

113mean vs median

An outlier drags the mean towards it but barely moves the median. If the two differ a lot, the data is skewed, so the median is usually the fairer typical value to report.

114pivot table

A tool that summarises a big table by grouping rows under row and column fields, then working out a sum, count or average for each group. Refresh it when the data changes.

115pattern, 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.

116correlation 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.

117conclusion

A statement that answers a question using evidence from the data, such as numbers. It should only say what the data shows and stay within the data's limits.

118Choosing the right chart

Pick the chart that matches your question: a bar chart to compare categories, a line chart for change over time, and a pie chart for parts of a whole.

119misleading chart

A chart whose design, like a cut-off axis, 3D effects or a cherry-picked range, makes the data look different from what it truly shows.

120Communicate insights

The final life cycle stage: present findings so the audience sees the answer to the original question. State the key finding first, link to the objective and note data limits.

121data science process

Work through a scenario in stages: define the objective, acquire data, validate and clean it, analyse it, report insights, and go back a stage if the data is poor.