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🚀 Course Goals
- Structure workbooks, tables and named ranges so AI assistance stays reliable and reproducible Build and test formulas against a case table agreed before any formula exists Clean and combine messy exports in Power Query with steps that re-run on next month's data Stand up a secure Excel-to-LLM bridge that keeps every provider key off the workbook
- Build and test formulas against a case table agreed before any formula exists
- Clean and combine messy exports in Power Query with steps that re-run on next month's data
- Stand up a secure Excel-to-LLM bridge that keeps every provider key off the workbook
📘 About This Course
Most "AI in Excel" material stops at the prompt. This course goes all the way to the number a manager will act on - and to the check that proves it.
You start in Excel itself: workbooks structured so AI assistance stays reliable, formulas that earn trust from a test table, Power Query pipelines that re-run next month, and PivotTables that cannot quietly report a stale cache. Then you build the piece that makes AI usable at work: a small, authenticated bridge on an Ubuntu lab that keeps every provider key server-side, exposes two narrow operations to Excel, and guards them with size limits, timeouts, redaction and a fixed response schema. GLM and Qwen sit behind the same three fields as any other OpenAI-compatible provider, so switching models is a configuration change on the server - not an edit to a single workbook.
From there the discipline repeats across real office work: grounded commentary where every claim cites the KPI it rests on, sales, HR, operations and administrative workflows with their own guardrails, Python in Excel reconciled to the cent against a PivotTable, an independent Ubuntu validation layer, and VBA and Office Scripts that put a weekly reporting pack behind one button - with a QA gate that blocks the export when a control total disagrees.
Every module is a real demonstration on synthetic data, including the failures: a merge that silently drops rows, a model that invents a ranking without a single wrong number in it, a type conversion that loses twenty euros, a duplicated export that inflates a week. You see the mistake, the diagnosis, and the fix - because that is what the job actually looks like.
You will not be asked to paste a provider key into a workbook, and you will not be asked to take a model's word for anything.
What You'll Learn
Structure workbooks, tables and named ranges so AI assistance stays reliable and reproducible
Build and test formulas against a case table agreed before any formula exists
Clean and combine messy exports in Power Query with steps that re-run on next month's data
Stand up a secure Excel-to-LLM bridge that keeps every provider key off the workbook
Route GLM, Qwen and any OpenAI-compatible provider through one endpoint and switch models server-side
Ground AI commentary in evidence and let a deterministic script, not the model, decide what passes
Use Python in Excel and an external Ubuntu validator to reconcile results to the cent
Automate a weekly reporting pack with VBA or Office Scripts behind a QA gate and an audit log
🛠️ Requirements
- Microsoft Excel for Microsoft 365 on Windows (Power Query, VBA and Office Scripts are used; Python in Excel where your licence allows it)
- A computer where you can install Python and run an SSH client
- Comfort with everyday Excel: entering formulas, sorting and filtering, saving workbooks
- No prior experience with AI APIs, Python or VBA is assumed - every tool is installed and configured on camera
👤 Who This Course is For
- Analysts, controllers and office professionals who already live in Excel and want AI they can defend in a review
- Finance, sales, HR and operations teams who must reconcile a number before anyone acts on it
- Anyone asked to "use AI" at work without putting company data or provider keys at risk
- Not for readers looking for prompt tricks: every claim here has to survive a deterministic check
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet