ACARA Digital Technologies Years 9–10 — Acquiring and Validating Data
Getting data from real sources and checking it's trustworthy.
Modules (121)
1
What data science is
2
The data science life cycle
3
Data science roles: scientist, engineer and analyst
4
What a research question is
5
Research question feature: measurability of data requirements
6
Data requirements for a research question
7
Data and information
8
Primary and secondary data sources
9
Quantitative data
10
Qualitative data
11
Purposes of qualitative and quantitative data
12
Text data: character and string
13
Numeric data: integer
14
Numeric data: floating point
15
Date/time data
16
Boolean data
17
Choosing a data type for a value
18
Collecting data with a survey
19
Writing unbiased survey questions
20
Population and sample
21
Piloting a survey before use
22
Validation rules on an online survey form
23
Linking an online form to a spreadsheet
24
Coding qualitative data: what a code is
25
Building a frequency table with COUNTIF
26
What a sensor is
27
Analogue sensor data and analogue-to-digital conversion
28
Matching sensors to the data they capture
29
Temperature sensor
30
Light sensor
31
Humidity sensor
32
Moisture sensor
33
Sensor data to monitor a person's health
34
Data collected over a period of time
35
Choosing a logging interval for sensor readings
36
Recording sensor readings in a spreadsheet with timestamps
37
Sensor errors: faults, calibration and noise
38
Open data portals and data licences
39
Searching and filtering a data repository
40
Authentic data in large repositories
41
Dataset file formats: CSV, XLSX, JSON and XML
42
Delimited (CSV) files: structure
43
Importing data from a CSV file
44
Acknowledging sources and copyright
45
Personal data
46
Consent when collecting personal data
47
Collecting only the data you need
48
Anonymising data
49
Ethical issue: ownership and control of data
50
Respectful data collection and Indigenous data sovereignty
51
De-identifying survey and interview data with participant codes
52
Spreadsheet rows, columns and cells
53
Entering data into cells
54
Header labels for rows and columns
55
Organising data in a table with headings
56
Keeping an untouched copy of the raw data
57
What a single-table database is
58
Database field data types
59
Primary key
60
What is a relational database?
61
Foreign key and table relationships
62
CREATE TABLE and INSERT INTO
63
Importing a CSV file into a database table
64
Validation versus verification
65
Validation: why data is checked on entry
66
Types of validation check
67
Setting spreadsheet data validation rules
68
Number range validation in a spreadsheet
69
Drop-down list validation in a spreadsheet
70
Validation messages and error alerts
71
Existence checking in a spreadsheet
72
Type checking in a spreadsheet
73
Range checking in a spreadsheet
74
Length checking with LEN
75
Format checking text and dates
76
The IF function in a spreadsheet
77
Flagging invalid entries with IF
78
Double entry check
79
Validation rules on database fields
80
SQL constraints for validation
81
Data integrity characteristic: authenticity
82
Data quality: accuracy
83
Data integrity characteristic: correctness
84
Data integrity characteristic: reasonableness
85
Data quality: relevance
86
Data integrity characteristic: timeliness
87
Data quality: bias
88
Data quality: reliability
89
Assessing data against the six integrity characteristics
90
Judging the quality of a dataset
91
Ethical issue: use of inaccurate or incomplete data
92
Cleaning data
93
Sorting data
94
Filtering data
95
Cleaning text with TRIM, UPPER and PROPER
96
Standardising spelling variations with Find and Replace
97
Removing duplicate rows
98
The COUNTIF function
99
Relative and absolute cell references
100
Finding duplicates with COUNTIF
101
Handling missing values
102
Converting text to numbers and fixing date formats
103
Splitting and joining text with LEFT, RIGHT and CONCAT
104
Splitting one column into several with Text to Columns
105
Formula error values and how to fix them
106
The MIN and MAX functions
107
Conditional formatting
108
Deciding what to do with an outlier
109
Keeping a cleaning log
110
Testing a formula against a hand-calculated answer
111
Checking end-to-end data integrity from collection to visualisation
112
The SUM function
113
Comparing mean and median
114
Pivot tables
115
Finding patterns, trends and outliers
116
Correlation is not causation
117
Drawing conclusions from data
118
Choosing the right chart
119
Spotting misleading charts
120
Communicating insights to an audience
121
Applying the data science process to a scenario
Smaller journeys, one per content description
The same modules, split up so a class can take just the content description it's working on.
6
ACARA 9-10 Digital Technologies: Planning a data science project — AC9TDI10P01
Students learn what data science is, how its life cycle works and how to turn a question into a measurable objective and a list of data requirements.
11
ACARA 9-10 Digital Technologies: Data, sources and data types — AC9TDI10P01
Students distinguish data from information, primary from secondary sources, and quantitative from qualitative data, and choose suitable data types for values.
8
ACARA 9-10 Digital Technologies: Acquiring data with surveys — AC9TDI10P01
Students design unbiased, well-sampled and piloted surveys, add validation rules to online forms, and turn responses into coded, counted data in a spreadsheet.
12
ACARA 9-10 Digital Technologies: Acquiring data with electronic sensors — AC9TDI10P01
Students learn how sensors capture data, choose sensors and logging intervals, record timestamped readings in a spreadsheet and watch for sensor errors.
7
ACARA 9-10 Digital Technologies: Data from online repositories and file formats — AC9TDI10P01
Students find, filter, assess, import and acknowledge datasets from open repositories in formats such as CSV.
7
ACARA 9-10 Digital Technologies: Privacy, consent and ethical data collection — AC9TDI10P01, AC9TDI10P14
Students apply privacy principles to collect only what they need, gain consent, anonymise data and respect ownership and Indigenous data sovereignty.
12
ACARA 9-10 Digital Technologies: Storing data in spreadsheets and databases — AC9TDI10P01, AC9TDI10P03
Students organise data in well-structured spreadsheets and model it in single-table and relational databases using keys, relationships and basic SQL.
17
ACARA 9-10 Digital Technologies: Validating data — AC9TDI10P01
Students apply validation checks in spreadsheets, online forms and databases to block or flag bad data, and see what validation can and cannot prove.
11
ACARA 9-10 Digital Technologies: Judging data quality and trust — AC9TDI10P01
Students test data against characteristics such as accuracy, authenticity, reasonableness, relevance, timeliness, bias and reliability to decide whether it can be trusted.
20
ACARA 9-10 Digital Technologies: Cleaning data with spreadsheet tools — AC9TDI10P01, AC9TDI10P02
Students clean data with sorting, filters, formulas and functions, handle duplicates, missing values and outliers, and record and check their work.
10
ACARA 9-10 Digital Technologies: Analysing data and completing the process — AC9TDI10P01, AC9TDI10P02
Students analyse and visualise cleaned data to find trends and outliers, draw and communicate conclusions, and apply the whole data science process to a scenario.
Printables for this journey
Every module above has its own free printable worksheet with an answer key. These crosswords and word searches cover the same terms:
✎
Years 5–6 Data & Information Crossword: Data, information and binary representation (Modules 1–10)
10 clues · Data and information, Types of data: numbers and text, Types of data: images, sound and video, Same data, different representations and more
✎
Years 5–6 Data & Information Crossword: Data, information and binary representation and Acquiring and manipulating data (Modules 11–20)
10 clues · Representing images with pixels, Primary and secondary data sources, Collecting data with a survey, Spreadsheet rows, columns and cells and more
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Years 5–6 Data & Information Crossword: Acquiring and manipulating data and Analysing and visualising data (Modules 21–30)
10 clues · Cleaning data, Formulas start with =, Arithmetic operators + - * /, The SUM function and more
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Years 5–6 Data & Information Crossword: Analysing and visualising data and Digital footprint and privacy (Modules 31–56)
10 clues · Choosing the right chart, Column and bar charts, Line charts, Pie charts and more
🔎
Years 5–6 Data & Information Word Search: Data, information and binary representation and Acquiring and manipulating data (Modules 1–20)
20 words to find · Data and information, Types of data: numbers and text, Types of data: images, sound and video, Same data, different representations and more
🔎
Years 5–6 Data & Information Word Search: Acquiring and manipulating data to Digital footprint and privacy (Modules 21–56)
20 words to find · Cleaning data, Formulas start with =, Arithmetic operators + - * /, The SUM function and more
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VCE Data Analysis Crossword: Types and purposes of data (Modules 3–14)
10 clues · Quantitative data, Qualitative data, Purposes of qualitative and quantitative data, Interviews to gather perspectives on a topical issue and more
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VCE Data Analysis Crossword: Types and purposes of data and Data types and data characteristics (Modules 15–24)
10 clues · Open data portals and data licences, Text data: character and string, Numeric data: integer, Numeric data: floating point and more
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VCE Data Analysis Crossword: Data types and data characteristics to Privacy and the Australian Privacy Principles (Modules 25–37)
10 clues · Characteristic of data: accessibility, Characteristic of information: clarity, Characteristic of information: context, Data quality: accuracy and more
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VCE Data Analysis Crossword: Privacy and the Australian Privacy Principles and Ethics and APA referencing (Modules 38–54)
10 clues · APP 2: anonymity and pseudonymity, Holding information only for its primary purpose, Security measures to protect personal information, Critiquing a system against the Australian Privacy Principles and more
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VCE Data Analysis Crossword: Ethics and APA referencing and Requirements, constraints and scope (Modules 56–67)
10 clues · Acknowledging sources and copyright, Purpose of APA referencing, APA in-text citations, APA reference list entry structure and more
🔎
VCE Data Analysis Word Search: Types and purposes of data and Data types and data characteristics (Modules 3–24)
20 words to find · Quantitative data, Qualitative data, Purposes of qualitative and quantitative data, Interviews to gather perspectives on a topical issue and more
🔎
VCE Data Analysis Word Search: Data types and data characteristics to Ethics and APA referencing (Modules 25–54)
20 words to find · Characteristic of data: accessibility, Characteristic of information: clarity, Characteristic of information: context, Data quality: accuracy and more
🔎
VCE Data Analysis Word Search: Ethics and APA referencing to Design tools for databases, spreadsheets and visualisations (Modules 56–80)
22 words to find · Acknowledging sources and copyright, Purpose of APA referencing, APA in-text citations, APA reference list entry structure and more
✎
VCE Spreadsheets, SQL & Visualisation Crossword: Spreadsheet structure, formulas and functions and Descriptive statistics (Modules 4–20)
10 clues · Naming worksheets consistently, Header labels for rows and columns, Relative and absolute cell references, The COUNTIF function and more
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VCE Spreadsheets, SQL & Visualisation Crossword: Descriptive statistics and Manipulating, validating and testing spreadsheet data (Modules 21–34)
10 clues · Grouping numbers into bins with COUNTIFS, The STDEV.S function, Interpreting standard deviation, Cleaning text with TRIM, UPPER and PROPER and more
✎
VCE Spreadsheets, SQL & Visualisation Crossword: Manipulating, validating and testing spreadsheet data and Relational database structure (Modules 35–44)
10 clues · Pivot tables, What is a relational database?, What a single-table database is, Database field data types and more
✎
VCE Spreadsheets, SQL & Visualisation Crossword: Relational database structure and Querying data with SQL (Modules 45–54)
10 clues · Importing a CSV file into a database table, Validation rules on database fields, SQL constraints for validation, What a database query is and more
✎
VCE Spreadsheets, SQL & Visualisation Crossword: Querying data with SQL to Purposes and types of data visualisations (Modules 55–70)
10 clues · SQL GROUP BY, SQL JOIN across two tables, SQL UPDATE and DELETE, Testing a query against the data and more
🔎
VCE Spreadsheets, SQL & Visualisation Word Search: Spreadsheet structure, formulas and functions to Manipulating, validating and testing spreadsheet data (Modules 4–34)
20 words to find · Naming worksheets consistently, Header labels for rows and columns, Relative and absolute cell references, The COUNTIF function and more
🔎
VCE Spreadsheets, SQL & Visualisation Word Search: Manipulating, validating and testing spreadsheet data to Querying data with SQL (Modules 35–54)
20 words to find · Pivot tables, What is a relational database?, What a single-table database is, Database field data types and more
🔎
VCE Spreadsheets, SQL & Visualisation Word Search: Querying data with SQL to Visualisation components and design conventions (Modules 55–86)
20 words to find · SQL GROUP BY, SQL JOIN across two tables, SQL UPDATE and DELETE, Testing a query against the data and more
✎
VCE Software Development U3O1 Crossword: Data structures and Data sources and files (Modules 98–113)
10 clues · Records: fields with different data types, Accessing a record's fields by name and by field index, Choosing a data structure, Plain text (TXT) files: structure and more
✎
VCE Software Development U3O1 Crossword: Data sources and files to Graphical user interfaces (Modules 114–124)
10 clues · Reading an XML file with ElementTree, Validation: why data is checked on entry, Types of validation check, Existence checking in code and more
🔎
VCE Software Development U3O1 Word Search: Pseudocode to Data sources and files (Modules 72–113)
21 words to find · Turning pseudocode into Python, Changing a global variable with the global keyword, Functions calling other functions, Program structure with a main() function and more
🔎
VCE Software Development U3O1 Word Search: Data sources and files to Object-oriented programming (Modules 114–141)
20 words to find · Reading an XML file with ElementTree, Validation: why data is checked on entry, Types of validation check, Existence checking in code and more
✎
VCE Software Development U4O1 Crossword: Code repositories, APIs, libraries and AI assistants to Beta testing (Modules 125–171)
10 clues · Keeping API keys secure, Validation versus verification, Alpha testing, Commenting out code to isolate a bug and more
🔎
VCE Software Development U4O1 Word Search: Code repositories, APIs, libraries and AI assistants to Beta testing (Modules 115–171)
22 words to find · Branches and merging, What a library is: standard vs third-party, Random numbers with import random, The math module and more
✎
VCE Data Analytics U3O1 Crossword: Data types and relational databases to Sourcing, validating and referencing data (Modules 17–49)
10 clues · Field sizes, SQL column data types and sizes, One-to-many relationships between tables, Boolean operators AND, OR and NOT in a WHERE clause and more
✎
VCE Data Analytics U3O1 Crossword: Sourcing, validating and referencing data to Statistical analysis of data (Modules 50–91)
10 clues · Setting spreadsheet data validation rules, Removing duplicate rows, Handling missing values, Converting text to numbers and fixing date formats and more
🔎
VCE Data Analytics U3O1 Word Search: Data types and relational databases to Statistical analysis of data (Modules 17–91)
20 words to find · Field sizes, SQL column data types and sizes, One-to-many relationships between tables, Boolean operators AND, OR and NOT in a WHERE clause and more
✎
VCE Data Analytics U3O2 Crossword: Research questions and project planning to Collecting primary and secondary data (Modules 3–31)
10 clues · What a research question is, Research question feature: clarity, Research question feature: measurability of data requirements, Research question feature: feasibility and more
✎
VCE Data Analytics U3O2 Crossword: Collecting primary and secondary data to Data integrity (Modules 32–75)
10 clues · Searching with keywords and search operators, Searching and filtering a data repository, One-dimensional arrays, Records: grouping fields of different data types and more
✎
VCE Data Analytics U3O2 Crossword: Data integrity to Evaluating designs and design tools (Modules 76–146)
10 clues · Data integrity: reasonableness, Data integrity: timeliness, Participant information statement, Consent form and more
🔎
VCE Data Analytics U3O2 Word Search: Research questions and project planning to Evaluating designs and design tools (Modules 3–146)
21 words to find · What a research question is, Research question feature: clarity, Research question feature: measurability of data requirements, Research question feature: feasibility and more
✎
VCE Data Analytics U4O1 Crossword: Audience and developing infographics and dashboards to Data quality and testing visualisations (Modules 63–83)
10 clues · Pagination of an infographic or dashboard, Clarifying data refresh requirements, Visual hierarchy in a visualisation, Clarity of message in a visualisation and more
🔎
VCE Data Analytics U4O1 Word Search: Audience and developing infographics and dashboards to Recording and assessing project progress (Modules 63–105)
20 words to find · Pagination of an infographic or dashboard, Clarifying data refresh requirements, Visual hierarchy in a visualisation, Clarity of message in a visualisation and more
✎
VCE Data Analytics U4O2 Crossword: Why data security matters and types of threats and Data integrity and its consequences (Modules 1–25)
10 clues · Goals and objectives of medium and large organisations, Safeguarding business operations, Mitigating financial loss and reputational damage, Accidental threats to data and more
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VCE Data Analytics U4O2 Crossword: Data integrity and its consequences and Evaluating security with CIA and security controls (Modules 26–43)
10 clues · Assessing data against the six integrity characteristics, Consequence of diminished data integrity: loss of reputation, Consequence of diminished data integrity: poor decision-making, Consequence of diminished data integrity: financial loss and more
🔎
VCE Data Analytics U4O2 Word Search: Why data security matters and types of threats to Evaluating security with CIA and security controls (Modules 1–43)
22 words to find · Goals and objectives of medium and large organisations, Safeguarding business operations, Mitigating financial loss and reputational damage, Accidental threats to data and more
✎
Years 9–10 Data & Information Crossword: Data compression to Acquiring, storing and validating data (Modules 17–51)
10 clues · Choosing a compression format for a task, Content, structure and presentation of a document, Restyling one document with a different stylesheet, Why separating content, structure and presentation matters and more
🔎
Years 9–10 Data & Information Word Search: Data compression to Data privacy and the Australian Privacy Principles (Modules 6–132)
21 words to find · Redundancy: repeated data that can be shortened, Lossless compression, Lossy compression, Run-length encoding: encoding text and more
✎
IGCSE Computer Science Paper 1 Crossword: 3.2 Input and Output Devices (Modules 273–282)
10 clues · 3D printer, Speaker, Actuator, Choosing a suitable output device for a context and more
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IGCSE Computer Science Paper 1 Crossword: 3.2 Input and Output Devices (Modules 283–292)
10 clues · Humidity sensor, Infra-red sensor, Level sensor, Light sensor and more
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IGCSE Computer Science Paper 1 Crossword: 3.2 Input and Output Devices and 3.3 Data Storage (Modules 293–303)
10 clues · Sensors, microprocessor and actuator in a control system, Matching sensors to the data they capture, Choosing the most suitable sensor for a given context, Choosing input devices, output devices and sensors together for a system and more
🔎
IGCSE Computer Science Paper 1 Word Search: 3.2 Input and Output Devices (Modules 262–282)
22 words to find · Choosing a suitable input device for a context, LCD screen, LED screen, Comparing LCD and LED screens and more
🔎
IGCSE Computer Science Paper 1 Word Search: 3.2 Input and Output Devices and 3.3 Data Storage (Modules 283–303)
22 words to find · Humidity sensor, Infra-red sensor, Level sensor, Light sensor and more
✎
IGCSE Computer Science Paper 2 Crossword: 7a Program Development Life Cycle (Modules 60–70)
10 clues · Re-asking for input until it is valid, Combining validation checks, What validation cannot do, Choosing a validation check for a field and more
🔎
IGCSE Computer Science Paper 2 Word Search: 7a Program Development Life Cycle (Modules 47–70)
20 words to find · REPEAT ... UNTIL loop (7a), Choosing between FOR, WHILE and REPEAT, Counting in a loop (7a), Totalling in a loop (7a) and more
✎
Crossword: Data Compression and Acquiring and Validating Data (Modules 226–254)
10 clues · Exporting audio at different quality levels in Audacity, How video compression reuses unchanged parts of frames, Streaming media and adjusting bitrate, Common compressed file formats and what they are used for and more
✎
Crossword: Acquiring and Validating Data (Modules 255–329)
10 clues · Validation rules on an online survey form, Choosing a logging interval for sensor readings, Recording sensor readings in a spreadsheet with timestamps, Sensor errors: faults, calibration and noise and more
✎
Crossword: Acquiring and Validating Data and Analysing and Visualising Data (Modules 333–377)
10 clues · Finding duplicates with COUNTIF, Splitting one column into several with Text to Columns, Deciding what to do with an outlier, Keeping a cleaning log and more
🔎
Word Search: Data Compression and Acquiring and Validating Data (Modules 226–329)
20 words to find · Exporting audio at different quality levels in Audacity, How video compression reuses unchanged parts of frames, Streaming media and adjusting bitrate, Common compressed file formats and what they are used for and more
🔎
Word Search: Acquiring and Validating Data and Analysing and Visualising Data (Modules 333–412)
20 words to find · Finding duplicates with COUNTIF, Splitting one column into several with Text to Columns, Deciding what to do with an outlier, Keeping a cleaning log and more
Frequently Asked Questions
Are these worksheets free?
Yes — every worksheet and its answer key is free to view, download, and print, with no account required to access any individual module.
Do I need an account?
No account is needed to view or print a worksheet. A free teacher account adds extras like assigning modules to a class and tracking progress automatically — it takes under three minutes to set up and doesn't need a credit card.
What year level is this for?
This curriculum is aligned to the Australian Curriculum (ACARA) and Victorian Curriculum, spanning roughly Years 3 to 10 — from first sequencing and variables through to object-oriented programming and advanced data structures. Start wherever matches your class's current level; each module builds on the last.
Can I print them?
Yes — every module has a one-click Print Worksheet button that generates a printable page with the practice questions and a full answer key.
Is this aligned to a curriculum?
Yes — the module sequence is mapped to the Australian Curriculum (ACARA) and Victorian Curriculum, and a class's progress is reported back against those curriculum bands (e.g. ACARA Years 5–6 sequencing and iteration, Years 7–8 functions and data structures), not just a generic difficulty scale.
Can students use this without a teacher?
Yes — students can start any module and practice directly, no class or account required. Joining a teacher's class with a class code adds progress tracking, class badges, and live Sprints, but isn't needed to work through the worksheets or modules solo.
Do I need to know ACARA Digital Technologies Years 9–10 myself to teach this?
No — every module comes with a full worked answer key, and Cloodit tracks who's stuck automatically, so you don't need to solve each exercise yourself before assigning it or be able to debug a student's work line by line.
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