TEST ONLY - Excel by Design | Training Proposal Generator - TEST ONLY

Important — Please Read Before Submitting All course content in Excel by Design: Real-World Analytical Skills is built and tested on Microsoft Excel for Windows with a Microsoft 365 subscription. If you are using Excel on a Mac, some features covered in the programme — including certain Power Query capabilities, form controls, and Developer ribbon tools — may behave differently or be unavailable. Training IT will discuss any Mac-specific limitations with you prior to commencement. A Microsoft 365 subscription is a baseline requirement for all units.

A note about this form. The first sections (contact details, role, delivery preferences, focus area), required fields have a red asterisk *.

Choose Your Course Content. When reviewing training needs the form offers multiple ways to define your training needs. There is no requirement to complete every field. To learn about the Modules review the form tab for an overview, or hover over the Info button when reviewing topics below.

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pick whole standard course modules with one click
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or simply leave it blank and let Training IT recommend a programme based on your role and business.

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Business or Personal? Choose Business if the training relates to your role at an organisation. Business Name becomes required and is used to help tailor the training content to your industry and context. Choose Personal if the training is for your own development outside a specific organisation. Business Name is optional in this mode and Training IT will tailor the proposal based on your role and stated objectives.

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Role / Job Title

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Forensic, Audit, or Risk and Compliance Work (optional)

If yes, Training IT will pair the programme with the Verdantia Carbon Solutions forensic dataset, the only forensic dataset in the collection.

Key business challenge or pain point

Choose Your Course Content

How to choose your content. This section is where you tell Training IT what you want to learn. You have three options, and you can mix them.

About each unit. Every unit is a focused, deep-dive session — not a shallow overview. Each unit is structured as one hour of guided, hands-on instruction. You will leave each session with a working knowledge of the topic and a completed exercise workbook to refer back to.

1. Pick whole days. Use the Select a Standard Course Module quick-pick row at the top of the next section to mark every unit in a full-day module as Must Learn. Pick one module, several, or none. Each clicked module covers a complete day of focused training (M4B is a half day). This is the fastest way to build a multi-day programme.

2. Refine individual units. Below the quick-pick, you will see 17 expandable module accordions covering 92 unit positions. Open any module, hover the i icon next to a unit for a description of what it covers, and set a priority of Must Learn, High Priority, Nice to Have, or Prior Knowledge. Prior Knowledge means you have already covered the topic; Training IT will exclude it from the recommendation. Leaving a unit unset means it is not prioritised.

3. Pick standalone units. If you do not want a full programme and only need a short, sharp session on a specific topic, toggle the stream to Standalone Units at the top of the next section. This is a different mode and shows you a curated list of role-relevant standalone topics rather than the full programme accordions.

If you are not sure — leave it all blank. Training IT will use your role, your business, and your business challenge to recommend a programme on your behalf. The skills section is optional throughout.

i Hover the i icon for a description of each unit. Set a priority or leave blank if not applicable.

Related Interests

Outside the programme? The items below are not covered in Excel by Design but are services Training IT offers separately. Tick any that apply and we will include them as a separate conversation item in our follow-up.

VBA and Macro Development Office Scripts Python in Excel Power BI

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For Training Managers: Complete the fields below and submit. Training IT will receive the details and follow up with a bespoke group programme proposal.

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Skill Assessment

Training IT offers a pre-training Excel skill assessment that helps identify the starting point for each participant and tailor module sequencing accordingly. The assessment is delivered as an Excel workbook completed independently before training commences.

Please send us the Excel skill assessment template so we can distribute it to participants prior to the programme commencing

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Important — Please Read Before Submitting All deliverables produced by Training IT are built and tested on Microsoft Excel for Windows with a Microsoft 365 subscription. If your organisation uses Excel on Mac or an older version of Office, please mention this in your enquiry so compatibility can be assessed during scoping.

Consulting and custom development: Use this form to start a conversation about a bespoke Excel model, Power BI report, or custom solution. Training IT will review your details and follow up with a scoping discussion.

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Excel Model Development Power BI Reporting Custom Dashboard Design Data Architecture and Cleanup Other

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Important — Please Read Before Submitting All training materials and bespoke datasets produced by Training IT are built and tested on Microsoft Excel for Windows with a Microsoft 365 subscription. If your participants will be using Excel on a Mac, or if your organisation runs an older version of Office, please mention this in your enquiry so compatibility can be assessed during scoping. A Microsoft 365 subscription is a baseline requirement for all Excel by Design content.

For organisations with tailored needs: The four standard tabs cover most enquiries. This tab is for situations where the standard catalogue does not quite fit — when you want your own data turned into a companion dataset, custom course content designed around a specific skill, your own pathway across the catalogue, or a custom Excel deliverable. Tick all that apply, fill in as much as you can, and Training IT will follow up with a discovery call before scoping the engagement.

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Your Own Companion Dataset
Your data, fictionalised and re-scaled, turned into an Excel by Design teaching dataset to V1 standard. Custom Course Content
A custom unit or module designed around a specific skill or business problem the V1 catalogue does not cover. Create Your Own Pathway
Custom mix-and-match across V1 modules and standalone units, sequenced to your audience and constraints. Custom Deliverable
A one-off Excel model, dashboard, or Power BI report. If this is your only need, the Consulting Enquiry tab is the lighter path.

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The Decision

The decision being made better. Not the topic. "We want our finance team to do better budget-versus-actual analysis" is a topic. "We want our finance business partners to defend a forecast variance in front of the executive without escalating to corporate finance" is a decision. The second is what this question is asking for.

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The Audience

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If your engagement involves source data files, three is enough for the initial enquiry. Training IT will request the full file inventory during the discovery call.

17 day-modules across 92 unit positions. Excel by Design: Real-World Analytical Skills is structured as 17 modules, with M4B delivered as a half-day format and M7B as a full day with a 3-hour Capstone. Each unit within a module is a focused one hour of guided, hands-on instruction. Participants leave each session with a working knowledge of the topic and a completed exercise workbook to refer back to. Programme length is flexible: most engagements run a tailored subset rather than the complete sequence, scaled to the audience, the timeline, and the budget.

The A and B pattern. Five of the 17 modules are split into A and B versions, and in every case the B is the extended-depth companion to the A. M4B builds on M4A's LAMBDA foundation by applying the same higher-order thinking to dynamic aggregation with GROUPBY and PIVOTBY. M5B extends M5A's GUI-driven Power Query into M-language, REGEX, external connections, and production ETL architecture. M6B extends M6A's core financial functions into planning, optimisation, and Australian payroll modelling under the legislated SGC rate path. M7B extends M7A's PowerPivot foundations and introductory DAX into advanced filter context (CALCULATE and the ALL family), time intelligence configured for the Australian 30/06 fiscal year-end, advanced text functions, and REGEX in DAX. M8B extends M8A's dashboard design fundamentals into interactive controls, dynamic column selection, and the IMAGE function for embedded media. The B modules are not parallel topics — they are the second half of a two-day deep dive on the same skill area, and they assume A-level fluency throughout.

Common conventions. Every module is delivered against real-looking Australian business data using the catalogue conventions: AUD currency, DD/MM/YYYY dates, July to June fiscal years, the legislated SGC rate path for superannuation, and state-based geography. Prestige AV is the master case study and is referenced from Module 1 through to Module 9; from Module 2 onwards, exercises can be delivered against an industry-relevant companion dataset to keep the learning anchored to the participant's own business context.

BASELINE indicates a foundation module recommended for all participants regardless of pathway. EXTENDS M[X]A indicates a module that builds on its A counterpart and assumes A-level fluency.

Foundation Modules

Full day — 6 units Recommended for all participants No prerequisites

What this module covers The foundation for everything that follows. M1 establishes the three-layer model architecture (data, calculation, presentation), spill-aware workbook design that cooperates with Excel 365's dynamic array engine, strategic protection and auditing patterns, refactoring of legacy nested-IF logic into modern structures, Boolean logic for clean conditional thinking, and conditional aggregation as the most-used advanced pattern in finance and operations work. Every other module in the programme assumes M1 fluency, and even advanced users routinely discover gaps in workbook design discipline that M1 surfaces. The day answers questions like: why do my formulas break when new data arrives, how do I make a model that auditors can verify, and how do I migrate a legacy spreadsheet to a dynamic-array architecture without rewriting it from scratch.

Unit-by-unit

M1.1 Spill-Aware Table DesignStructured tables, @implicit intersection, spill range (#)
Redesign workbooks so they cooperate with the dynamic array engine; eliminate #SPILL! errors by design.
M1.2 Strategic Model Design — Architecture and Input IntegrityNamed ranges, data validation, three-layer architecture
Apply a clean input / calculation / output separation that other people can use without breaking.
M1.3 Strategic Model Design — Protection, Auditing and DocumentationSheet protection, formula auditing tools, documentation standards
Lock down inputs and outputs, document assumptions, and produce models an auditor can verify.
M1.4 Refactoring Legacy Conditional LogicIFS, SWITCH, replacing nested IF chains
Convert hard-to-read nested IFs into modern, maintainable conditional structures.
M1.5 Boolean LogicAND, OR, NOT, Boolean arithmetic
Build clean conditional logic using truth values rather than tangled IF nests.
M1.6 Conditional AggregationSUMIFS, COUNTIFS, AVERAGEIFS, MAXIFS, MINIFS
Total, count and average against multiple criteria — the most commonly used advanced pattern in finance and operations.

Full day — 7 units Requires M1 The day-one workhorse module

What this module covers The day-one workhorse module covering modern Excel 365 lookup and transformation patterns. XLOOKUP replaces VLOOKUP and INDEX/MATCH with cleaner syntax and better behaviour at edges. Error handling and isolation patterns prevent #N/A and #REF errors from propagating through models. Dynamic filtering with FILTER returns matching rows without pivot tables or helper columns. Dynamic sorting and unique extraction enable formula-driven dropdowns and report headers that update automatically. Pattern generation with SEQUENCE produces date series, numbered ranges, and grid coordinates; modern text processing rounds out the toolkit. Every analyst working in Excel 365 needs these patterns — this is the module that converts a pre-365 user into a 365-fluent one and unlocks everything that follows.

Unit-by-unit

M2.1 Modern Lookup FunctionsXLOOKUP, XMATCH
Replace VLOOKUP and INDEX/MATCH with the modern equivalent that handles missing matches and reverse lookups cleanly.
M2.2 Error HandlingIFERROR, IFNA, error isolation
Stop #N/A and #REF errors propagating through dependent formulas and reports.
M2.3 Dynamic Filtering with FILTERFILTER with single and compound criteria
Return matching rows from any dataset based on one or more conditions; results update as source data changes.
M2.4 Dynamic SortingSORT, SORTBY
Build sorted views that recalculate automatically — no more manual re-sorting after data updates.
M2.5 Extracting Unique ValuesUNIQUE, deduplication patterns
Generate deduplicated lists from any column, ideal for dropdown sources and dynamic report headers.
M2.6 Pattern Generation with SEQUENCESEQUENCE, EDATE, date and integer series
Produce date series, numbered ranges, and the column or row scaffolding for forecasts and timelines.
M2.7 Text Processing (bonus unit)TEXTSPLIT, TEXTBEFORE, TEXTAFTER, TEXTJOIN
Parse, split and reassemble text fields without nested LEFT/RIGHT/MID gymnastics.

Full day — 6 units Requires M1, M2 Multi-source consolidation without Power Query

What this module covers The toolkit for treating arrays as first-class data structures. M3 covers the full set of array reshaping operations: stack arrays vertically and horizontally with VSTACK and HSTACK, slice arrays with TAKE, DROP, CHOOSECOLS and CHOOSEROWS, reshape one-dimensional lists into grids with WRAPCOLS and WRAPROWS, flatten multi-dimensional arrays with TOCOL and TOROW, and resize and trim arrays with EXPAND and the TRIMRANGE dot operator. Particularly valuable for analysts who consolidate data from multiple sources without Power Query — quarterly files, multi-region reporting, branch-level rollups — and for anyone reshaping data to feed into pivot tables, dashboards, or downstream calculations. The module bridges M2 and M4A: it teaches the array literacy that LAMBDA and the higher-order functions assume.

Unit-by-unit

M3.1 Array Combination — VSTACK and HSTACKVSTACK, HSTACK, array combination patterns
Combine multiple arrays vertically or horizontally without pivot tables or Power Query — useful for quarterly consolidation and multi-region reporting.
M3.2 Array Extraction — TAKE and DROPTAKE, DROP, head and tail slicing
Extract leading or trailing rows or columns from an array; the formula equivalent of head and tail operations.
M3.3 WRAPCOLS and WRAPROWSWRAPCOLS, WRAPROWS, dimensional reshaping
Reshape one-dimensional arrays into two-dimensional grids by wrapping — convert long lists into tabular layouts for reporting.
M3.4 Column and Row Selection — CHOOSECOLS and CHOOSEROWSCHOOSECOLS, CHOOSEROWS, named selection
Pick specific columns or rows from a larger array to build custom report layouts dynamically.
M3.5 Array Flattening — TOCOL and TOROWTOCOL, TOROW, multi-dimensional to linear
Flatten two-dimensional grids into single columns or rows; useful for unpivoting in formula form and building input lists.
M3.6 Array Resizing — EXPAND and TRIMRANGEEXPAND, TRIMRANGE dot operator
Pad arrays to a target size with EXPAND or trim trailing blanks dynamically with TRIMRANGE for live-sized ranges.

Functional Programming and Aggregation (M4 Pair)

Full day — 6 units Requires M1, M2 The functional programming layer of Excel 365

What this module covers M4A brings functional programming into Excel. LET names sub-expressions to eliminate formula repetition; LAMBDA defines reusable custom functions that the workbook treats as first-class objects. MAP applies a LAMBDA element-wise across an array; REDUCE accumulates values across an array with an initial seed, the foundation of running totals and conditional aggregates; SCAN builds running-balance and running-state arrays with reset logic; BYROW and BYCOL apply LAMBDA logic row-wise or column-wise to wide tables. The combination eliminates helper columns, eliminates repeated formula edits when business rules change, and enables a personal function library that can be published for the rest of the team. This module is the entry point to writing code-quality Excel and the prerequisite for every formula-driven analytical pattern that follows in M4B, M6B, and M7.

Unit-by-unit

M4A.1 LET and LAMBDALET for named sub-expressions, LAMBDA syntax, parameter definition
Eliminate formula repetition with named sub-expressions; define reusable custom functions the workbook calls by name.
M4A.2 Advanced LAMBDA TechniquesRecursive LAMBDA, LAMBDA as argument, function libraries
Build recursive LAMBDA functions, pass LAMBDAs as arguments, and design a personal function library used through M4 and beyond.
M4A.3 Array Transformation with MAPMAP applying LAMBDA element-wise
Apply a LAMBDA to every element of an array — the equivalent of dragging a formula down a column, in a single cell.
M4A.4 Array Accumulation with REDUCEREDUCE with initial value, accumulation patterns
Accumulate running totals and conditional aggregates across arrays with a defined starting value.
M4A.5 Running Calculations with SCANSCAN running totals, running-state logic
Build running balances that reset at fiscal year boundaries, threshold calculations, and stateful sequence logic.
M4A.6 BYROW and BYCOLBYROW and BYCOL applying LAMBDA row-wise and column-wise
Find the maximum, sum, or aggregate per row or column of a wide table without a helper column.

Half day — 3 units + 3-hour Capstone Builds on M4A LAMBDA fluency The post-PivotTable era of Excel

What this module covers M4B is the extended-depth companion to M4A, applying higher-order thinking to data aggregation and replacing the traditional pivot table with formula-driven equivalents. MAKEARRAY generates programmatic two-dimensional arrays from row and column index formulas, useful for multiplication tables, distance matrices, and amortisation grids. GROUPBY aggregates data dynamically (a pivot table in a formula) with PERCENTOF for share-of-total calculations and full support for custom aggregation LAMBDAs. PIVOTBY builds two-axis summary tables in formula form with rows and columns both grouped, totals and subtotals included, and full dynamic refresh. Together these eliminate the need for traditional pivot tables in many reporting contexts and produce summaries that update automatically as source data grows — without any rebuild step.

Unit-by-unit

M4B.1 Array Generation with MAKEARRAYMAKEARRAY, LAMBDA index functions
Generate two-dimensional arrays from row and column indices for multiplication tables, distance matrices, and amortisation grids.
M4B.2 Dynamic Grouping with GROUPBYGROUPBY with aggregate functions, PERCENTOF
Group and aggregate data dynamically — like a pivot table in a formula. Returns a spilled result that updates automatically.
M4B.3 PIVOTBY — Multi-Dimensional SummariesPIVOTBY with row and column grouping
Build two-axis summary tables in a formula — rows and columns both grouped, with totals and subtotals, fully dynamic.
M4 Capstone (concept stage)3-hour integrative exercise combining M4A and M4B techniques
Closes the half-day. A facilitated end-to-end Prestige AV scenario where participants apply LAMBDA architecture, higher-order functions, and dynamic GROUPBY/PIVOTBY aggregation in concert to a single deliverable.

Power Query (M5 Pair)

Full day — 6 units Requires M1, M2 The data-prep day, GUI-driven

What this module covers The data-prep day. M5A covers Power Query foundations through to advanced GUI-driven transformations, without writing M code. Connect to CSV, Excel, and folder-based sources; reshape data with unpivot, column promotion, and type changes; merge queries with the four join kinds (inner, left outer, right outer, anti) and learn when each is appropriate; append queries to stack quarterly or branch files into a single tall table; build advanced transformations through the ribbon (conditional columns, custom columns, parameter-driven steps); and parameterise file paths so the same query works across environments. By the end of the module participants can build production-quality data pipelines that refresh on demand, replacing manual copy-paste consolidation entirely. Heavy use of the Greenfield Resource Recovery dataset for hands-on consolidation work.

Unit-by-unit

M5A.1 Power Query FoundationsConnect, import, shape from CSV and Excel
The Power Query interface, query editor, and the foundational transform-and-load pattern that underpins every later unit.
M5A.2 Data Reshaping — UnpivotUnpivot Columns, Promote Headers, Change Type
Convert wide crosstab layouts into tall analytical tables ready for formula or pivot analysis.
M5A.3 Merge Queries — JoinsMerge Queries, Join Kind, Expand Table Column
Combine two tables on a common key using inner, left outer, right outer, and anti joins — the Power Query equivalent of VLOOKUP or SQL JOIN.
M5A.4 Stacking Data — Append QueriesAppend Queries, Folder connector, File.Contents
Stack multiple period files, region files, or worksheets into a single tall table — essential for multi-period and multi-branch reporting.
M5A.5 Advanced TransformationsConditional Column, Custom Column, Group By
Build conditional logic, derived columns, and grouped summaries in the query editor without dropping into M code.
M5A.6 Parameters and Dynamic SourcesParameters, dynamic file path, Manage Parameters
Replace hardcoded file paths with parameters so the same query works across environments — the foundation of portable, maintainable pipelines.

Full day — 5 units + 1-hour Recap Requires M5A Code-level Power Query

What this module covers M5B is the extended-depth companion to M5A, taking participants from GUI-driven Power Query into M-language code. M language fundamentals (let expressions, custom function syntax, the role of each generated step); advanced text functions for parsing, splitting, and standardising messy data with Text.Split, Text.Contains, and the List functions; pattern matching with REGEX through Text.RegEx for ABN validation, postcode extraction, and product code parsing; external connections to SharePoint lists, OData feeds, web APIs and SQL databases; and production ETL architecture covering query folding, error handling, the four-tier source-staging-transform-output pattern, refresh order management, and pipeline maintainability. This is the module for analysts who have hit the limits of the GUI and need to write Power Query that scales, performs, and survives a handover to another team member.

Unit-by-unit

M5B.1 Introduction to the M LanguageM syntax, let expressions, custom step writing
Read and modify the M code Power Query generates behind the scenes; write custom steps that the GUI cannot produce.
M5B.2 Advanced Text Functions in Power QueryText.Split, Text.Contains, List functions
Parse, split, and classify text directly in M for messy real-world data preparation.
M5B.3 Pattern Matching with REGEXText.RegEx, pattern extraction and classification
Use regular expressions in M for ABN validation, postcode extraction, and pattern-based field parsing.
M5B.4 External ConnectionsSharePoint, OData, web APIs, SQL
Connect Power Query to live external sources beyond local files — without hardcoded URLs or credentials.
M5B.5 Production ETL ArchitectureFour-tier source / staging / transform / output, refresh order
Structure a production-quality query architecture that can be handed over, refreshed reliably, and maintained over time.
M5 Recap (concept stage)1-hour facilitated consolidation of M5A and M5B
Closes the day. A walk-through that ties the M5A visual-transformation surface back to the M5B M-language and architecture work, with reference patterns for choosing between GUI and code-level approaches in production engagements.

Financial Functions (M6 Pair)

Full day — 6 units Requires M1, M2 The numerical-analysis day

What this module covers The numerical-analysis day, covering the complete toolkit for financial calculation. Time value of money (PV, FV, NPV, XNPV, IRR, XIRR) with Australian fiscal year considerations and date-irregular cash flow handling; loan and lease mechanics including amortisation schedules, principal-and-interest splits, effective rate calculations, and PMT-based scenario modelling; depreciation under straight-line, reducing-balance, units-of-production, and AASB 116 component methods; investment analysis with NPV, IRR, MIRR, and payback period; foundational business statistics covering descriptive statistics, distribution shape, correlation and basic regression; and the Analysis ToolPak for descriptive statistics, regression, correlation, and exponential smoothing. The numerical foundation that finance, treasury, and investment analysts work with daily — and the prerequisite for M6B's planning and optimisation work.

Unit-by-unit

M6A.1 Time Value of MoneyPV, FV, NPV, XNPV, IRR, XIRR
Calculate present and future values, NPV, and IRR — including the date-irregular variants critical for real-world cash flow analysis.
M6A.2 Loan and Lease MechanicsPMT, IPMT, PPMT, RATE, NPER, CUMIPMT, CUMPRINC
Build amortisation schedules; split principal and interest; calculate effective rates for comparison across structures.
M6A.3 Depreciation SchedulesSLN, DB, DDB, SYD, AASB 116 component method
Build straight-line, reducing-balance, and component-based depreciation schedules aligned to Australian accounting standards.
M6A.4 Investment AnalysisNPV, IRR, MIRR, payback, profitability index
Compare investment alternatives using the standard analytical toolkit; understand the differences between NPV and IRR ranking.
M6A.5 Business StatisticsAVERAGE, MEDIAN, STDEV, VAR, CORREL, descriptive statistics
The statistical foundations: distribution shape, central tendency, dispersion, and correlation for business data.
M6A.6 Analysis ToolPakDescriptive Statistics, Regression, Correlation, Exponential Smoothing
Use the Analysis ToolPak add-in for one-shot regression, correlation matrices, and time-series smoothing.

Full day — 5 units + 1-hour Wrap-Up Requires M6A The decision-support day

What this module covers M6B is the extended-depth companion to M6A, moving beyond core calculation into planning, scenario, and optimisation work. Database functions (DSUM, DCOUNT, DAVERAGE, DGET) for criteria-driven summaries when conditional aggregation alone falls short; sensitivity analysis with Goal Seek for break-even and target-seeking calculations; strategic scenario modelling with Scenario Manager and one- and two-variable data tables for management committee presentations; payroll and superannuation modelling under the legislated Australian SGC rate path (9.0 percent in FY2014 through to 12.0 percent from FY2025), including PAYG, payroll tax thresholds by state, leave accrual, and termination payments; and Solver for constrained optimisation problems (production scheduling, portfolio allocation, resource constraints, transportation problems). The module that turns analysts from calculators into decision-support partners — the critical step from "I produced the number" to "I helped the executive decide".

Unit-by-unit

M6B.1 Database FunctionsDSUM, DCOUNT, DAVERAGE, DGET, criteria ranges
Use database functions for criteria-driven summaries that conditional aggregation cannot easily replicate.
M6B.2 Sensitivity Analysis with Goal SeekGoal Seek, break-even calculations, target-seeking
Find the input value that produces a target output — the foundation of break-even and target-seeking analysis.
M6B.3 Strategic Scenario ModellingScenario Manager, Data Tables, Best/Base/Worst
Build best-case, base-case, and worst-case scenarios; produce sensitivity tables ready for management committee presentations.
M6B.4 Payroll, Superannuation and Cost ModellingSGC rate path, PAYG, payroll tax, leave accrual
Model Australian payroll under the legislated SGC rate trajectory, payroll tax thresholds by state, and award-based leave accrual.
M6B.5 SolverSolver, constraints, objective cells, Simplex LP and GRG Nonlinear
Solve constrained optimisation problems: production scheduling, portfolio allocation, resource allocation, transportation problems.
M6 Wrap-Up (concept stage)1-hour facilitated consolidation of M6A and M6B
Closes the day. Ties the M6A core finance toolkit back to the M6B planning and optimisation work, with a decision rubric for when to reach for Goal Seek, Scenario Manager, or Solver on real-world planning problems.

Data Modelling and Analysis (M7 Pair)

Full day — 6 units Requires M1, M2; M5A recommended The data-modelling foundation

What this module covers The data-modelling foundation. M7A covers building star-schema data models inside Excel using PowerPivot, establishing relationships between fact and dimension tables, and writing the foundational DAX measures. Two practical-use sessions reinforce the model design and DAX measure work with hands-on Prestige AV scenarios. The module closes with GETPIVOTDATA for pulling measure values into custom report layouts that bypass the pivot table grid entirely. M7A is the strongest preparation in the programme for participants moving into Power BI — the DAX engine and data model are identical between the two products.

Unit-by-unit

M7A.1 PowerPivot BasicsPowerPivot ribbon, Data Model, calculated columns vs measures
The PowerPivot interface, the Data Model concept, and the foundational distinction between calculated columns and measures.
M7A.2 Data Model RelationshipsStar schema, fact and dimension tables, relationship cardinality
Build a star-schema data model with fact tables, dimension tables, and the relationships between them.
M7A.3 Practical Use of PowerPivotApplied PowerPivot scenarios, model navigation, diagram view
Hands-on reinforcement of the M7A.1 and M7A.2 foundation through worked Prestige AV scenarios. Covers model navigation, the diagram view, and the analyst workflow inside the Data Model.
M7A.4 DAX Measures IntroductionSUM, COUNT, CALCULATE, basic measures, measure organisation
Write the foundational DAX measures. Covers measure organisation through display folders and the kpi_ prefix convention.
M7A.5 Practical PowerPivot Extended with DAX MeasuresApplied DAX scenarios, measure libraries, executive summary patterns
Extend the M7A.4 measure library to a full executive summary layout. Twelve-measure reference library with display folders and prefix conventions.
M7A.6 GETPIVOTDATAGETPIVOTDATA, custom report layouts
Pull measure values into custom report layouts that bypass the pivot table grid entirely.

Half day — 4 units + 3-hour Capstone + 1-hour Wrap-Up Requires M7A The advanced DAX day

What this module covers The advanced DAX day. M7B covers the filter context model that defines DAX behaviour, time intelligence functions configured for the Australian 30 June fiscal year-end through the 30/06 argument, DAX text manipulation for in-model parsing and standardisation, and the REGEX functions for pattern-based validation and extraction. The module closes with a 3-hour integrative capstone exercise that combines all M7A and M7B techniques into a production-grade Prestige AV scenario. A 1-hour M7 Wrap-Up consolidation walk-through is delivered separately to round out the M7 pair.

Unit-by-unit

M7B.1 Filter ContextCALCULATE, FILTER, ALL, ALLSELECTED, context transition
Master the filter context model that defines DAX behaviour; modify context with CALCULATE, FILTER and the ALL family. The row-context-to-filter-context transition is the central mental model.
M7B.2 Time Intelligence in DAXTOTALYTD with 30/06 argument, DATEADD, SAMEPERIODLASTYEAR, DATESYTD, DATESBETWEEN
Build YTD, prior period, and moving average measures configured for the Australian fiscal year — TOTALYTD requires the 30/06 argument.
M7B.3 Advanced Text Functions in DAXDAX text functions, COMBINEVALUES, SUBSTITUTE, FORMAT
Apply DAX text manipulation for product code parsing, customer name standardisation, and category derivation directly inside the data model.
M7B.4 Pattern Matching with REGEX in DAXREGEXTEST, REGEXEXTRACT, REGEXREPLACE, XLOOKUP match_mode=3
Use regular expressions in DAX for ABN format validation, product code parsing, and pattern-based lookup.
M7B Capstone (concept stage)3-hour integrative exercise combining M7A and M7B techniques
Closes the day. A facilitated production-grade Prestige AV scenario where participants build a star-schema data model, write a complete DAX measure library with filter context and time intelligence, and deliver a reportable executive summary.
M7 Wrap-Up (concept stage)1-hour facilitated consolidation of M7A and M7B
A walk-through delivered separately to round out the M7 pair. Ties the M7A foundation work to the M7B advanced patterns with reference architecture for analysts moving into Power BI.

Dashboards and Delivery (M8 Pair)

Full day — 6 units Requires M1, M2 The output-design day

What this module covers The output-design day. M8A covers building professionally designed visual reports. Dynamic charts and sparklines tied to structured table sources so they extend automatically as data grows; formula-driven conditional formatting for heat maps, exception highlighting, and pattern-based rules where the formula evaluates against every cell in the applied range; the design principles that separate professional dashboards from amateur ones (visual hierarchy, alignment, colour discipline, the three-zone layout, restraint with grid lines and chart junk); dashboard architecture with form input pages (FIPs), navigation tabs, and the explicit connection between user inputs and calculated outputs; advanced chart types including combination, waterfall, bullet, and small multiples with dynamic titles and axis controls; and one- and two-variable data tables for sensitivity and scenario analysis on the dashboard surface. The module that teaches design thinking alongside technical skill — most participants discover their existing dashboards have been working harder than necessary.

Unit-by-unit

M8A.1 Getting Started with Visual ReportingCharts, Sparklines, dynamic named ranges
Build dynamic charts and sparklines tied to structured table data; introduce dynamic chart sources and basic dashboard layout.
M8A.2 Enhanced Conditional FormattingFormula-based rules, data bars, colour scales, icon sets
Apply formula-driven conditional formatting to highlight patterns, flag exceptions, and build heat maps.
M8A.3 Professional Dashboard DesignLayout principles, colour architecture, IMAGE function basics
The design principles that separate professional dashboards from amateur ones — hierarchy, alignment, colour discipline, three-zone layout.
M8A.4 Dashboard Architecture (FIP)FIP design, named ranges, worksheet architecture
Design the full dashboard structure — navigation tabs, a dedicated form input page, and the connection between user inputs and calculated outputs.
M8A.5 Designing Advanced ChartsCombination charts, waterfall, bullet chart, dynamic titles
Build combination, waterfall, bullet, and small-multiple charts with dynamic titles, axis formatting, and connections to dashboard controls.
M8A.6 Data Tables and ScenariosData Tables (one-variable, two-variable), Scenario Manager
Build sensitivity tables that show how outcomes change across a range of inputs; scenario modelling for financial and operational decisions.

Full day — 5 units Requires M8A The delivery-mechanics day

What this module covers M8B is the extended-depth companion to M8A, taking dashboards from static designs into interactive tools. Hyperlinks and shape-based navigation for multi-page workbook front pages, with documented patterns for back-to-home buttons and section navigation; interactive controls including form controls (dropdowns, scroll bars, option buttons), slicers driven by tables and pivot tables, and the native Excel checkboxes introduced in 365 — all linked to cells and used in formulas; dynamic column selection where the displayed columns change based on a user selection through CHOOSECOLS, FIELDVALUE, XMATCH, and Linked Data Types; PIVOTBY visualisation as a chart source that produces dynamic charts from formula output rather than pivot tables; and the IMAGE function for displaying images from URLs or SharePoint paths directly in cells, including dynamic image lookup tied to XLOOKUP. The module that transforms a dashboard from a viewing surface into a tool the audience actively uses.

Unit-by-unit

M8B.1 Hyperlinks and Workbook NavigationHYPERLINK, shape buttons, worksheet navigation
Build a navigation system using HYPERLINK formulas, shape-based buttons, and worksheet tabs — the front page of a multi-page workbook.
M8B.2 Interactive ControlsSlicers, Form Controls, native Checkboxes (Developer ribbon)
Add slicers, form controls (dropdown, scroll bar, option button), and native Excel checkboxes; link controls to cells and drive formulas from their values.
M8B.3 Dynamic Column SelectionCHOOSECOLS, XMATCH, FIELDVALUE, Linked Data Types
Build reports where displayed columns change based on a user selection; introduce Linked Data Types for record-style datasets.
M8B.4 PIVOTBY VisualisationPIVOTBY, spill range chart source (#)
Use PIVOTBY output as a chart source to build dynamic visual summaries that update without a traditional pivot chart.
M8B.5 Importing and Displaying Data with IMAGEIMAGE, URL-based image display, XLOOKUP image lookup
Import and display images from URLs or SharePoint paths directly in worksheet cells; combine with XLOOKUP for dynamic image lookup.
M8B.6 Module 8 Wrap-UpM8 consolidation walk-through, production readiness checklist, integration patterns
One-hour facilitator-led consolidation session integrating all M8A dashboard architecture and M8B interactive controls techniques into a single production-ready Financial Insights Pro walk-through.

AI-Augmented Workflows

Full day — 6 units Requires M1 to M5A minimum Capstone for the full programme

What this module covers The capstone module for the full programme: practical integration of AI tools (Microsoft Copilot, Claude) into production Excel work. AI foundations and formula development from natural-language prompts, including Copilot Agent Mode for multi-step transformations, prompt patterns that produce reliable output, and the validation discipline that separates production AI use from improvisation; AI-assisted Power Query M code generation and debugging with prompt patterns specific to data transformation work; AI-assisted DAX measure creation with the rigour required to catch filter context errors before they reach a report; AI-assisted dashboard layout planning and chart-type selection through the iteration loop of suggestion plus human design judgement; a structured risk framework covering the eight error types most likely to reach board-level reports undetected, and the pre-deployment validation checklist; and a capstone project that applies AI-augmented techniques across the full data-to-dashboard pipeline. The module that converts AI from improvisation into production discipline. Requires comfort with M1 to M5A as a minimum.

Unit-by-unit

M9.1 AI Foundations and Formula DevelopmentCopilot, Agent Mode, prompt patterns, formula validation
Use Copilot to develop formulas from natural-language prompts; Agent Mode for multi-step transformations; the validation discipline that makes AI safe.
M9.2 AI for Power Query and M CodeAI prompt patterns for M, M code generation, transformation validation
Use AI to generate and debug M code for Power Query — prompt patterns specific to ETL work and the validation discipline for AI-generated pipelines.
M9.3 AI for DAX and PowerPivotAI prompt patterns for DAX, measure validation, filter context debugging
Generate, validate, and debug DAX measures using AI; the rigour required for filter context errors and Australian fiscal year time intelligence.
M9.4 AI for Dashboard DesignAI design review, layout suggestions, chart type selection
Use AI to plan dashboard layouts, suggest chart types, and review design decisions; the iteration loop of AI suggestion plus human design judgement.
M9.5 AI Production Workflow and Risk AssessmentRisk taxonomy, validation checklist, error classification
Apply a structured risk framework to AI-generated Excel content — the eight error types most likely to reach board-level reports undetected.
M9.6 AI CapstoneIntegrated project, full programme synthesis
Apply AI-augmented techniques across a full end-to-end project — Power Query ingestion, formula design, DAX measures, and dashboard delivery.

36 purpose-built Australian datasets. Every unit in Excel by Design: Real-World Analytical Skills is taught using real-looking Australian business data. All datasets share consistent conventions — AUD currency, DD/MM/YYYY dates, July to June fiscal years, SGC superannuation, and state-based geography. This panel is for reference only and helps you identify which datasets are most relevant to your participant group. Dataset 34, Verdantia Carbon Solutions, is the only forensic dataset in the collection — purpose-built for forensic accounting, internal audit, and risk and compliance audiences. Datasets 35 (Melba Meadows Cellar Door) and 36 (Lyrebird Asset Advisory) are the newest additions to the catalogue — Melba Meadows anchors the Modelling Pathway as a compact six-hour build, and Lyrebird is the strongest fit in the collection for hierarchy-driven analytics, lifecycle cost modelling, and asset-management dashboards.

Prestige AV Pty Ltd

Commercial AV Integration — Retail, B2B and B2C

Baseline — all participants

FY2016 to FY2026 110,000+ rows 17 sheets All modules

Prestige AV is the programme's primary case study company — a fictional eight-branch commercial AV and home theatre retailer operating nationally across Victoria, NSW, Queensland, SA and WA. Every participant works with this dataset from Module 1 regardless of their sector or learning path. It provides a stable, familiar reference point that carries through the full 92-unit curriculum across 17 day-modules. From Module 2 onwards, exercises can be delivered using an industry-relevant companion dataset alongside or instead of Prestige AV, and participants immediately begin connecting the skill to their own business context.

What makes it analytically rich: dual product and service revenue streams each with their own price history, a B2B debtors ledger for credit management, eight-branch location hierarchy, and 11 years of trading history covering two ownership transitions and the COVID period.

Finance and Financial Services

FY1975 to FY2026 — 52 years 178,270 rows Wealth management, AUM, loan book

What makes this dataset analytically distinct The longest dataset in the collection at 52 years — the only one that crosses multiple economic cycles. AUM waterfall, loan book with covenant testing, deferred tax schedule, and a shareholder register with capital structure history. The v2 extension adds group consolidation workings with intercompany eliminations and minority interest — the most complex financial accounting scenario in the collection.

Best module fit: Modules 1, 2, 6, 7 — Finance and Accounting pathway, Capital Structure exercises

FY2010 to FY2026 13 sheets Lending and broking divisions

What makes this dataset analytically distinct Two-division structure: a Lending Division earning net interest margin (NIM) and a Broking Division earning trail and upfront commission. Loan register with 13,140 origination records across 14 years — the only dataset in the collection built around mortgage economics. Covers loan-to-value ratios, arrears classification, serviceability, and credit quality segmentation.

Best module fit: Modules 1, 2, 6 — Finance and Accounting pathway, Lending and Broking organisations

Claims development triangle Actuarial reserve modelling

What makes this dataset analytically distinct The only dataset in the collection built around actuarial reserving. Includes a loss development triangle for IBNR estimation — a triangular data structure not found in any other dataset and one of the most challenging Excel modelling scenarios for finance and insurance professionals. Covers written vs earned premium, claims development by accident year, and underwriting result.

Best module fit: Modules 1, 6, 7 — Insurance, Risk and Actuarial pathway

Healthcare and Aged Care

FY2015 to FY2026 30,000+ rows Physiotherapy, OT, Psychology

What makes this dataset analytically distinct Three distinct funding streams in a single dataset: Medicare, NDIS, and WorkCover — each with its own item numbers, rebate rates, and billing rules. Appointment-level records with provider, patient, service type, duration, and funding source. Clinician productivity as a billable-hours KPI unique to this dataset. The rebate-vs-gap-fee split requires multi-condition lookup logic that translates directly to any professional services billing environment.

Best module fit: Modules 1, 2, 7 — Healthcare and NDIS pathway

FY2005 to FY2026 — 22 years 47,138 rows Residential aged care and NDIS

What makes this dataset analytically distinct Dual aged care and NDIS funding streams with government-regulated schedule rates (ACFA and NDIS price guide). The only dataset where revenue rates are set by external regulation, not commercial negotiation. Care hours records for staff-to-resident ratios. Covers resident dependency classification, NDIS plan budget drawdown, and funding claim processing — a realistic mirror of how aged care finance teams actually manage reporting.

Best module fit: Modules 1, 2, 6, 7 — Healthcare and Aged Care pathway

Retail and Consumer

FY2008 to FY2026 — 19 years 103,897 rows + NPS extension (127,848) Fashion, footwear, homewares

What makes this dataset analytically distinct Omnichannel retail with in-store and online channels, markdown events, and inventory movement. The NPS extension adds 127,848 individual survey responses with CSAT, CES, driver analysis, and demographic segmentation — the largest single-topic dataset in the collection and the primary vehicle for customer analytics exercises. Covers return rates and the analytical challenge of distinguishing seasonal markdown from demand-driven inventory clearance.

Best module fit: Modules 2, 4, 8 — Retail and Customer Analytics pathway

FY2011 to FY2026 — 16 years Multi-store hardware retail Parquet + CSV + Excel format mix

What makes this dataset analytically distinct The primary large-volume ETL teaching dataset for Module 5. The full 16-year sales history is held in 16 annual Parquet files (215 MB total) — scale that makes Power Query the necessary tool, not just an option. The master Excel file is a single-year indexed extract used for formula exercises. Covers category mix, store performance, and seasonal trade patterns typical of hardware and trade retail.

Best module fit: Module 5 (Power Query at scale), Module 2 — Retail and Trade pathway

FY2024 to FY2026 — 3 years 3,497 rows Member cooperative, gallery, print

What makes this dataset analytically distinct The second-smallest dataset in the collection by design — a member co-operative running a gallery, print studio, and framing workshop. Covers member dues, consignment sales splits, grant income, and volunteer hour tracking. Suited to small business, arts organisations, and NFP professionals who find large commercial datasets disorienting. Also used in contexts where participants need to work with limited, irregular, or manually-managed data.

Best module fit: Modules 1, 4, 6 — Arts, NFP and Small Business pathway

FY2020 to FY2026 actual + FY2027 forecast ~276,000 rows across 19 CSV files Star schema (purpose-built for Power BI)

What makes this dataset analytically distinct The only dataset in the collection delivered as a clean star schema across 19 CSV files — purpose-built for beginner Power BI cohorts who need to load fact and dimension tables into a data model. Embedded COVID impact, seasonal event spikes (Mother's Day, Valentine's Day), and an FY2027 forecast layer make it ideal for time-series analysis and forecasting work. Suits floristry, e-commerce, and seasonal small-to-medium retail audiences.

Best module fit: Modules 5, 7, 8 — Power BI beginner pathway, E-commerce and Seasonal Retail

Construction, Property and Facilities Management

FY2005 to FY2026 — 22 years 34,689 rows Project register, progress claims, variations

What makes this dataset analytically distinct The primary commercial entity is a Project Register, not a product or service catalogue — a structural shift that changes how every aggregation is built. Progress Claims for milestone-based billing, Variations for scope changes, and Retention tracking. The only dataset in the collection where revenue is recognised by construction milestone rather than transaction. Directly relevant to builders, subcontractors, project managers, and quantity surveyors.

Best module fit: Modules 1, 2, 7 — Construction and Project Management pathway

FY2010 to FY2026 — 17 years 25,109 rows Recurring contracts, SLA compliance

What makes this dataset analytically distinct Depots replace branches as the location hierarchy. Recurring contract revenue with SLA compliance tracking — on-time completion rates, penalties, and contract renewal analysis. Services and Addons as separate catalogues (core versus ancillary). The SLA compliance dimension introduces a KPI calculation pattern — actual performance vs contractual threshold — directly transferable to any operations or service delivery environment.

Best module fit: Modules 1, 2, 5, 8 — Operations and Property Services pathway

Operations, Logistics and Manufacturing

FY2010 to FY2026 — 17 years 519,000+ rows — 71.9 MB 19 depots, all states

What makes this dataset analytically distinct The largest dataset in the collection. At 519,000+ rows it is specifically designed to demonstrate the performance implications of formula-based vs Power Query approaches — scale that makes ETL the only practical option. Consignment-level ledger with origin, destination, weight, freight class, and revenue per job. Driver performance data for on-time delivery, fuel efficiency, and incidents. The asset extension adds an AASB 16 lease workings structure unique to this dataset.

Best module fit: Modules 2, 5, 6 — Operations and Logistics pathway, ETL capstone

FY2008 to FY2026 — 19 years 24,948 rows Job costing, BOM, OEE, supply chain

What makes this dataset analytically distinct The only dataset where three cost layers — raw material, direct labour, and manufacturing overhead — are all separately recorded at the individual job level. Machine OEE (Overall Equipment Effectiveness) is unique across the entire collection. Bill of Materials for multi-level product composition. The steel price index ties raw material costs to market movements, making margin compression in FY2022 analytically traceable to its source. The v2 extension adds procurement analytics: EOQ, ROP, and safety stock calculations.

Best module fit: Modules 2, 4, 7 — Manufacturing and Operations pathway

FY1996 to FY2026 — 31 years 76,970 rows across 4 files EPA compliance, WHS, dual revenue streams

What makes this dataset analytically distinct The most structurally complex operational dataset: EPA waste categories and a Compliance Register (regulatory framework), a Gate Fee Schedule and Recycling Commodity Index (dual revenue streams), and three distinct data quality eras built into the history. Commercial invoice line items aggregate through three levels — line item to invoice to contract to site P&L — the most complex multi-level aggregation exercise in the collection. The WHS incident records include structured narratives, root causes, and corrective actions.

Best module fit: Modules 2, 4, 5 — Operations and Environmental pathway, ETL capstone

FY2010 to FY2026 — 17 years ~751,000 rows across 31 files Multi-franchise, integrated GL ecosystem

What makes this dataset analytically distinct A regional Wimmera-Mallee heavy equipment dealership representing four OEM franchises (agricultural and construction). Four-funder floor plan financing, integrated general ledger across 31 files, and full multi-franchise reconciliation. The largest multi-file ecosystem in the collection by file count — purpose-built for advanced Power Query work where the ETL itself is the analytical exercise. Suits dealerships, multi-franchise operators, and analysts working with integrated GL reconstructions.

Best module fit: Modules 5, 6, 7 — Multi-franchise Operations, Floor Plan Finance, advanced ETL pathway

Professional Services

FY2010 to FY2026 — 17 years 171,673 rows Time billing, lock-up days, disbursements

What makes this dataset analytically distinct Time Entries are the primary revenue transaction — hours billed, not product sales. This structural shift changes every aggregation pattern in the dataset. Disbursements as a separate on-charged cost stream. Lock-up days (WIP + debtors days) as the defining KPI for law firm financial management. Directly relevant to any professional services or consulting firm that bills by time — accounting practices, engineering consultancies, HR consulting, and IT services.

Best module fit: Modules 1, 2, 8 — Professional Services pathway

FY2016 to FY2026 16,626 rows Event-based revenue, casual workforce

What makes this dataset analytically distinct Menus replace products as the primary catalogue and Event Transactions are the revenue unit — per-event billing rather than per-sale. Casual workforce with variable hours and weekly payroll creates a workforce analytics challenge not found in the salaried datasets. COVID event cancellations in FY2020 create a structurally interesting forecasting problem. Revenue per guest and contribution per event type are the key KPIs.

Best module fit: Modules 1, 2, 4 — Hospitality and Events pathway

FY2011 to FY2026 — 16 years ~209,000 rows across 20 sheets 5-level asset hierarchy, ~24,400 components

What makes this dataset analytically distinct The only dataset in the collection where strategic asset management is the entire business. A Melbourne-based boutique advisory delivering ISO 55001 aligned Strategic Asset Management Plans, condition assessments, lifecycle cost modelling, and AASB 116 revaluations across eleven private-sector client portfolios. The five-level asset hierarchy (Portfolio, Facility, System, Sub-System, Component) is the deepest in the catalogue, supporting hierarchy-aware lookups and parent-child DAX patterns that no other dataset reproduces at scale. The condition inspection register (~60,800 records) supports time-series degradation analysis, and the renewal forecast schedule generates the canonical 50-year infrastructure renewal mountain chart. Suits utilities, transport, mining services, large facility owners, and any audience working with fixed asset registers, useful-life assumptions, and renewal capex programmes.

Best module fit: Modules 6B, 7, 8A — Asset Management Analytics, Hierarchical DAX, Lifecycle Cost Modelling, Renewal Mountain Dashboards

Technology and Telecommunications

FY2015 to FY2026 — 12 years 29,239 rows ARR, MRR, churn, NRR, CRM pipeline

What makes this dataset analytically distinct The only dataset built around SaaS subscription economics. ARR waterfall, MRR by plan and product, churn rate, Net Revenue Retention, customer health scoring, and CRM pipeline conversion — the complete SaaS metrics framework. Three products launched across different years (ApexHR FY2015, ApexFinance FY2017, ApexOps FY2020) create a multi-cohort ARR analysis that is unique in the collection. Directly applicable to any subscription or recurring revenue business.

Best module fit: Modules 1, 2, 8 — Technology and SaaS pathway

FY2013 to FY2026 — 14 years MVNO — virtual mobile operator Subscriber economics, churn, ARPU

What makes this dataset analytically distinct A Mobile Virtual Network Operator — no physical location hierarchy, revenue driven by monthly recurring plan fees. The Churn Register enables net adds, churn rate, and ARPU calculations. Wholesale cost for host carrier network access creates a margin-by-plan analysis unique in the collection. Network SLA introduces service quality KPI modelling. Applicable to any subscription or recurring billing business beyond telecoms.

Best module fit: Modules 2, 4, 8 — Technology and Subscription pathway

Media, Publishing and Advertising

FY2012 to FY2026 — 15 years 27,069 rows 4 revenue streams — subscriptions, advertising, licensing, events

What makes this dataset analytically distinct The broadest revenue mix of any dataset: subscriptions, advertising, content licensing, and events in a single company. The structural media shift is analytically visible — print advertising falls to 0.20 of its 2012 index level by FY2026 while digital grows. Events revenue is zero in FY2020 and FY2021 (COVID), creating a structural forecasting problem that flat-rate extrapolation cannot handle. Subscription cohort model with quarterly churn and NRR adds a second subscription economics dataset alongside Apex SaaS.

Best module fit: Modules 2, 4, 8 — Media and Digital Business pathway

Agriculture and Primary Industry

FY2000 to FY2026 — 27 years 17,952 rows Cropping, livestock, commodity prices, water markets

What makes this dataset analytically distinct Properties replace branches and Enterprises define farming activity types (cropping, livestock, horticulture). Commodity price index and rainfall index as external production variables — the only dataset in the collection where two independent environmental factors simultaneously drive revenue and yield outcomes. Seasonal events (planting, harvest, weather incidents) and water usage add dimensions found nowhere else. Suited to agribusiness, rural finance, and regional banking analysts.

Best module fit: Modules 1, 2, 4 — Agriculture and Agribusiness pathway

Multi-file ecosystem — 43 files across 4 waves No single master workbook BOM, HACCP, cold store, maintenance

What makes this dataset analytically distinct The only dataset with no master workbook — 43 separate files across four waves covering truck logs (Parquet and CSV), WHS incidents (PDF), food safety audits, production schedules, cold store movements, and HACCP monitoring. This multi-source architecture mirrors how dairy and food manufacturing businesses actually store operational data. Bill of Materials links ingredients to production at SKU level. Suited to food manufacturing, supply chain, and WHS professionals.

Best module fit: Module 5 (multi-source ETL), Modules 2, 7 — Food Manufacturing and Supply Chain pathway

Plantation forestry Harvest cycles, species yield, product grading

What makes this dataset analytically distinct Plantation-based revenue with long harvest and replanting cycles that introduce multi-period lifecycle modelling not found in any other dataset. Species yield and product grade add a multi-tier product hierarchy. Carbon sequestration credits as a secondary revenue stream unique in the collection. Suited to forestry, agricultural, and environmental organisations working with long-cycle asset planning and natural resource management.

Best module fit: Modules 2, 4, 6 — Primary Industry and Environmental pathway

FY2016 to FY2026 Mine sites as location hierarchy Production, royalties, capital projects, safety KPIs

What makes this dataset analytically distinct Revenue net of government royalty deductions — a unique revenue model in the collection and the only dataset where a mandatory government deduction sits between gross revenue and operating income. Capital Projects as a separate investment track for mine development. The most detailed WHS safety KPI structure in the entire collection, reflecting the high-risk workplace context of underground and open-cut mining.

Best module fit: Modules 2, 4, 7 — Mining and Resources pathway

Tourism, Travel and Hospitality

FY1996 to FY2025 — 30 years 100,823 rows Dual currency, dual fiscal year, international bookings

What makes this dataset analytically distinct Three distinct tour types (Day, Bus, International) each with their own booking table — a structural pattern found nowhere else in the collection. Flight and Hotel Packages as a fourth revenue stream. The New Zealand business entity introduces dual-currency (AUD and NZD) and dual-fiscal-year complications that test date intelligence in DAX and formula design. 30 years of history spanning multiple tourism cycles including the Ansett collapse, GFC, and COVID.

Best module fit: Modules 1, 2, 5 — Tourism and International pathway

6 CSV source files — no single master workbook ~870,000 rows across 12 files Construction phase + operational phase

What makes this dataset analytically distinct Multi-source file architecture — six CSV files and six Excel files with no single master workbook — mirroring how hotel property management and revenue management systems actually export data. Construction phase dataset (FY2019 to FY2020) covers the two-year build before the property opens, making it suitable for capital project modelling exercises distinct from operational analysis. Revenue management metrics including RevPAR, ADR, and occupancy rate are unique to this dataset.

Best module fit: Module 5 (multi-source ETL), Module 8 — Hospitality and Revenue Management pathway

Nov 2024 to Dec 2025 — 14 months 406 rows across 5 sheets Daily takings, simple workforce, sole-trader scale

What makes this dataset analytically distinct The smallest and simplest dataset in the collection by design — a specialty coffee cafe with daily takings, a small casual workforce, and supplier purchases. Purpose-built as the introductory companion for participants who would be overwhelmed by Prestige AV's scale: small business owners, sole traders, and learners doing a single M1 plus M2 day. Covers cash and card splits, day-of-week patterns, casual loading, and weekly margin tracking.

Best module fit: Modules 1, 2 — Small Business and introductory single-day engagements

Wine and Beverage

FY2027 to FY2029 — 3 years (investment + ramp-up + stabilised) Single workbook, ~210 input cells Stage 1 of the Modelling Pathway — six-hour build

What makes this dataset analytically distinct The catalogue's compact from-scratch construction dataset, sized for a single training day. A small Yarra Valley wine business with a freehold property, an established mailing-list label of 1,800 cases per year, and an AUD 480,000 cellar-door investment decision under consideration. Unlike every other dataset in the collection, Melba Meadows ships with the input data block populated and the calculation and output sheets left empty for the participant to build into. Participants build an integrated income, cash, and balance sheet model end-to-end across three financial years, producing a binary recommendation supported by a two-variable sensitivity grid. Industry-specific mechanics include Wine Equalisation Tax (WET) at 29 percent, the WET Producer Rebate, AASB 116 component depreciation across capital works and fit-out, and small-business company tax at 25 percent. Suits any audience that needs to construct (rather than only analyse) bounded investment cases — small business owners, finance teams in small enterprises, advisory professionals, and learners stepping up to financial modelling for the first time.

Best module fit: Modelling Pathway Stage 1 — Compact From-Scratch Construction. Bridges the foundation modules (M1, M2) into the analytical and modelling modules (M6A, M6B).

Energy and Utilities

Solar generation and battery storage Power Purchase Agreements Feed-in tariffs, LGCs

What makes this dataset analytically distinct Revenue from Power Purchase Agreements (PPAs), feed-in tariffs, and Large-scale Generation Certificates (LGCs) — three revenue streams unique to the renewable energy sector. Generation data by site with capacity factor and output against contracted volume. Suitable for energy retailers, utilities, infrastructure investors, and sustainability reporting teams. Covers the analytical challenge of reconciling metered generation against contracted volume and market price exposure.

Best module fit: Modules 2, 6, 7 — Energy and Utilities pathway

Real Estate Development and Investment

FY2022 to FY2035 — 14 years (incl. forecast) ~3,900 formulas across 68 sheets 6 linked workbooks — capital stack and operations

What makes this dataset analytically distinct The most architecturally complex dataset in the collection. A single-asset Special Purpose Vehicle for a Perth CBD luxury hotel development, modelled across six interlinked workbooks: site acquisition due diligence, capital stack waterfall, construction draws, operational forecast (USALI), debt service schedule, and consolidated returns. Honest negative project IRR finding deliberately preserved. Suits real estate developers, project finance professionals, infrastructure investors, and analysts working with linked-workbook financial models.

Best module fit: Modules 1, 6, 8 — Real Estate Development, Capital Stack, Linked Workbook Architecture

Public Sector, Government and Not-for-Profit

FY2010 to FY2026 — 17 years Rate-based revenue model Infrastructure, community services, compliance

What makes this dataset analytically distinct Rate-based revenue is structurally distinct from all commercial datasets — rates are calculated by land valuation and rate category, not by sales transactions. Covers capital works programme, community services delivery, infrastructure depreciation, and regulatory compliance reporting. The superannuation rate reference (SGC Reference sheet) is the authoritative historical super rate table for the entire collection. Suited to local government, state government agencies, and public sector analysts.

Best module fit: Modules 1, 2, 7 — Local Government and Public Sector pathway

FY2013 to FY2026 — 14 years No P&L — income by source, expenditure by program Endowment funds, donations, volunteers

What makes this dataset analytically distinct The only dataset with no P&L structure — it uses an NFP income and expenditure model with endowment funds, government contracts, individual donations, and in-kind contributions. Volunteer hour tracking and grant acquittal reporting create analytical patterns found nowhere else in the collection. Suited to charities, community foundations, member associations, peak bodies, and any organisation that reports to a funding body rather than shareholders.

Best module fit: Modules 1, 4, 6 — NFP and Community Foundation pathway

FY2000 to FY2026 — 27 years Primary, secondary, TAFE, university Multi-file ecosystem — four data waves

What makes this dataset analytically distinct Covers all four Victorian public education tiers — primary schools, secondary schools, TAFE institutes, and a metropolitan university — under a single fictional state authority. Multi-file ecosystem mirrors how real education analysts work across separate HRMS, Student Information System, FMIS, and compliance systems. Enrolment trends, NAPLAN-style outcomes, staff FTE, facility utilisation, and grant funding by program make this the most analytically diverse government dataset in the collection.

Best module fit: Modules 2, 5, 7 — Government and Education pathway

Forensic Accounting, Internal Audit, Risk and Compliance

FY2020 to 14 November 2023 (voluntary administration) 13,990 rows across 24 sheets Eight discrepancy vectors deliberately encoded

What makes this dataset analytically distinct The only forensic dataset in the collection. The records pack of a fictional Australian carbon credits brokerage and ESG advisory in voluntary administration, with eight discrepancy vectors deliberately encoded across the staff master, general ledger, bank statements, and superannuation lodgements. Participants work the dataset as analysts engaged by the administrator, surfacing the discrepancies through FILTER discipline (M2), array reshaping (M3), multi-source merge (M5A), M-code level reconciliation (M5B), and cross-table DAX (M7). The recommended pairing with Verdantia is M2 to M7 as a five-day forensic review programme. Triggered in this form via the separate forensic, audit, or risk and compliance question — not the industry dropdown — because it is audience-led not industry-led.

Best module fit: Modules 2, 3, 5A, 5B, 7 — Forensic Accounting, Internal Audit, Risk and Compliance pathway