VCE Data Analytics - U3O1 - Data Analytics

1 Interpreting teacher-provided requirements for a data solution 2 Functional requirements 3 Solution Non-functional Requirements 4 Writing measurable non-functional requirements 5 Constraints on a solution 6 Scope of a solution 7 Listing requirements and constraints for a problem 8 Text data: character and string 9 Numeric data: integer 10 Numeric data: floating point 11 Date/time data 12 Boolean data 13 Choosing a data type for a value 14 What is a relational database? 15 What a single-table database is 16 Database field data types 17 Field sizes 18 SQL column data types and sizes 19 Primary key 20 Foreign key and table relationships 21 One-to-many relationships between tables 22 CREATE TABLE and INSERT INTO 23 SQL SELECT and FROM 24 SQL WHERE conditions 25 Boolean operators AND, OR and NOT in a WHERE clause 26 SQL ORDER BY 27 SQL JOIN across two tables 28 Exporting query results for use in a spreadsheet 29 Data dictionary 30 Writing a data dictionary entry 31 Input-process-output (IPO) chart 32 Writing an IPO chart for a spreadsheet or database task 33 Query design 34 Designing a query from a requirement 35 Mock-up of a spreadsheet layout 36 Annotated diagram of a database or spreadsheet 37 Interpreting a given design 38 Primary and secondary data sources 39 Authentic data in large repositories 40 Open data portals and data licences 41 Dataset file formats: CSV, XLSX, JSON and XML 42 Importing data from a CSV file 43 Importing a CSV file into a database table 44 Judging the quality of a dataset 45 Validation: why data is checked on entry 46 Types of validation check 47 Existence checking in a spreadsheet 48 Type checking in a spreadsheet 49 Range checking in a spreadsheet 50 Setting spreadsheet data validation rules 51 Validation rules on database fields 52 SQL constraints for validation 53 Purpose of APA referencing 54 APA in-text citations 55 APA reference list entry structure 56 APA reference for a dataset or report 57 APA reference for a web page 58 APA referencing of AI-generated content 59 Formulas start with = 60 Arithmetic operators + - * / 61 Copying a formula down a column 62 Relative and absolute cell references 63 The SUM function 64 The AVERAGE function 65 The MIN and MAX functions 66 The COUNT function 67 The COUNTIF function 68 The SUMIF function 69 The IF function in a spreadsheet 70 Looking up values with XLOOKUP or VLOOKUP 71 Sorting data 72 Filtering data 73 Cleaning data 74 Cleaning text with TRIM, UPPER and PROPER 75 Removing duplicate rows 76 Handling missing values 77 Converting text to numbers and fixing date formats 78 Splitting and joining text with LEFT, RIGHT and CONCAT 79 Formatting numbers and cells 80 Formula error values and how to fix them 81 The MEDIAN function 82 Comparing mean and median 83 Range of a data set 84 Building a frequency table with COUNTIF 85 Grouping numbers into bins with COUNTIFS 86 The STDEV.S function 87 Interpreting standard deviation 88 Pearson's correlation coefficient (r) with CORREL 89 Interpreting the value of r 90 Scatter graphs 91 Trendlines on a chart 92 Correlation is not causation 93 Histograms 94 The shape of a data distribution 95 Skew in data 96 Finding patterns, trends and outliers 97 Drawing conclusions from data 98 Purpose of a data visualisation: exploratory data analysis 99 Purpose of a data visualisation: presentation of information 100 Purpose of a data visualisation: interactive exploration 101 Matching a visualisation to its purpose and audience 102 Long-form infographic 103 Series of infographic posters 104 Dashboards 105 Static, interactive and live data visualisations 106 Dynamic data visualisations 107 Pivot tables 108 Slicers and interactive filters 109 Design Tool - Mock-ups 110 Mock-up of a data visualisation 111 Design Tool - Storyboards 112 Choosing the right chart 113 Column and bar charts 114 Line charts 115 Pie charts 116 Chart titles, axis labels and legends 117 Visualisation component: text and graphics 118 Visualisation component: tables 119 Visualisation component: charts and graphs 120 Using colour in a visualisation 121 Choosing fonts for a visualisation 122 Using images and icons in a visualisation 123 Consistent layout and style across a visualisation 124 Visual hierarchy in a visualisation 125 Clarity of message in a visualisation 126 Conditional formatting 127 Spotting misleading charts 128 Justifying functions, formats and conventions 129 How does an AI actually work? 130 Artificial intelligence features in software tools 131 Writing a prompt to generate a data visualisation 132 Refining a visualisation prompt 133 Checking an AI-generated visualisation against the data 134 Data generated by artificial intelligence 135 Machine learning for predictions 136 Statistical modelling to make predictions 137 Making predictions with FORECAST 138 Predictions, decisions and recommendations from models 139 Limits and reliability of predictions 140 Data quality: bias 141 Choosing a range of test data 142 Boundary test values 143 Test cases: input, expected output, actual output 144 Testing a formula against a hand-calculated answer 145 Testing a query against the data 146 Constructing test data for validation checks 147 Visual inspection of a data visualisation 148 Confirming charts and graphs represent the data

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VCE Data Analytics U3O1: Requirements, constraints and scope Students learn to write functional and non-functional requirements and to separate constraints and scope from what a data solution must do.

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VCE Data Analytics U3O1: Data types and relational databases Students learn the characteristics of text, numeric and Boolean data and how data types, field sizes, tables and primary and foreign keys structure a relational database.

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VCE Data Analytics U3O1: SQL queries and database design tools Students learn to write SQL queries with SELECT, WHERE, ORDER BY and INNER JOIN, and to plan databases and spreadsheets with data dictionaries, IPO charts, query designs and layout diagrams.

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VCE Data Analytics U3O1: Sourcing, validating and referencing data Students learn to find authentic data in large repositories, download and import it, check it with existence, type and range validation, and credit sources using APA referencing.

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VCE Data Analytics U3O1: Spreadsheet formulas and cleansing data Students learn to use spreadsheet formulas and functions, sorting and filtering, and cleansing techniques to calculate with data and fix errors in it.

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VCE Data Analytics U3O1: Statistical analysis of data Students learn to use descriptive statistics, Pearson's correlation coefficient, and the shape and skew of data to identify trends, relationships and patterns.

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VCE Data Analytics U3O1: Purposes and types of data visualisations Students learn the purposes of data visualisations and the differences between infographics, dashboards and dynamic visualisations.

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VCE Data Analytics U3O1: Designing data visualisations Students learn to plan visualisations with mock-ups and storyboards and to apply charts, colours, fonts, images, visual hierarchy and clarity of message for their intended users.

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VCE Data Analytics U3O1: Artificial intelligence trends in data analytics Students learn how AI features in software tools, prompts for visualisations, machine learning and statistical modelling are used to make predictions, decisions and recommendations.

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VCE Data Analytics U3O1: Testing databases, spreadsheets and visualisations Students learn to test formulas, queries, validation and visualisations using test data and test cases that compare expected and actual results.

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VCE Innovative Solutions Crossword: Impact of Emerging Trends in Digital Systems and more (Part 1 of 2) 10 clues · Impact of Emerging Trends in Digital Systems, Characteristics of Creative and Innovative Solutions, Functions and Capabilities of Digital Systems, Ideation Techniques and more

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VCE Innovative Solutions Crossword: Gantt Charts and Project Management and more (Part 2 of 2) 10 clues · Gantt Charts and Project Management, Design Tool - Mock-ups, Design Tool - Storyboards, Design Tool - Sitemap and more

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VCE Innovative Solutions Word Search: Impact of Emerging Trends in Digital Systems and more (All modules) 20 words to find · Impact of Emerging Trends in Digital Systems, Characteristics of Creative and Innovative Solutions, Functions and Capabilities of Digital Systems, Ideation Techniques and more

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Issues in Technology Crossword: Is AI using too much power? and more (Part 1 of 2) 10 clues · Is AI using too much power?, How does an AI actually work?, Compression - losing quality or saving space?, Why file size matters and more

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Issues in Technology Word Search: Is AI using too much power? to What is RADCAB? (All modules) 22 words to find · Is AI using too much power?, How does an AI actually work?, Compression - losing quality or saving space?, Why file size matters and more

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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

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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

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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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Years 5–6 Creating Digital Solutions Crossword: Defining problems and requirements and Designing algorithms with branching and iteration (Modules 1–10) 10 clues · What is a problem a digital system can solve?, User need, Writing a problem statement, Functional requirements and more

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Years 5–6 Creating Digital Solutions Word Search: Defining problems and requirements and Designing algorithms with branching and iteration (Modules 1–20) 20 words to find · What is a problem a digital system can solve?, User need, Writing a problem statement, Functional requirements 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: 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

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VCE Data Analysis Crossword: Requirements, constraints and scope and Design tools for databases, spreadsheets and visualisations (Modules 68–80) 10 clues · Scope of a solution, Listing requirements and constraints for a problem, Interpreting teacher-provided requirements for a data solution, Input-process-output (IPO) chart and more

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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

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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

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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

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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

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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

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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

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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

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VCE Spreadsheets, SQL & Visualisation Crossword: Purposes and types of data visualisations and Visualisation components and design conventions (Modules 71–86) 10 clues · Purpose of a data visualisation: informing, Purpose of a data visualisation: entertaining, Purpose of a data visualisation: persuading, Matching a visualisation to its purpose and audience and more

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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

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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

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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

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VCE Software Development U3O1 Crossword: Requirements and design tools to Naming conventions and internal documentation (Modules 10–47) 10 clues · Data dictionary, Writing a data dictionary entry, Designing classes with a class diagram, Object descriptions and more

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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

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VCE Software Development U3O1 Crossword: Errors and debugging and Testing modules (Modules 168–177) 10 clues · Debugging a program with print() and trace tables, Setting a breakpoint, Stepping through code and watching variables, Tracing nested loops and more

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VCE Software Development U3O1 Word Search: Requirements and design tools to Pseudocode (Modules 10–71) 21 words to find · Data dictionary, Writing a data dictionary entry, Designing classes with a class diagram, Object descriptions and more

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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

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VCE Software Development U3O1 Word Search: Errors and debugging to Artificial intelligence in programming (Modules 168–193) 20 words to find · Debugging a program with print() and trace tables, Setting a breakpoint, Stepping through code and watching variables, Tracing nested loops and more

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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

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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

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Crossword: Statistical analysis of data to Designing data visualisations (Modules 94–128) 10 clues · The shape of a data distribution, Skew in data, Purpose of a data visualisation: exploratory data analysis, Purpose of a data visualisation: presentation of information and more

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Crossword: Artificial intelligence trends in data analytics and Testing databases, spreadsheets and visualisations (Modules 130–148) 10 clues · Artificial intelligence features in software tools, Writing a prompt to generate a data visualisation, Refining a visualisation prompt, Checking an AI-generated visualisation against the data and more

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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

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Word Search: Statistical analysis of data to Testing databases, spreadsheets and visualisations (Modules 94–148) 20 words to find · The shape of a data distribution, Skew in data, Purpose of a data visualisation: exploratory data analysis, Purpose of a data visualisation: presentation of information and more