What this course is about
How to turn a table of numbers into clear conclusions: what happened, why it might have happened and what to do next. And how not to fall for a neat but wrong conclusion.
Numbers say nothing on their own. “229 requests in March”: is that a lot or a little? Good or bad? Your manager needs an answer: what changed, why, and what to do about it. {AI} is a good helper on this path: it spots patterns, suggests explanations and drafts conclusions. But AI easily mistakes a coincidence for a cause and confidently forecasts what cannot be forecast. In this course you will learn to get real value from it and to stop it in time.
What you will learn
- What the numbers show
- Example: requests by topic over six months
- Comparing periods and groups
- Example: which district is “worse”
- Looking for causes: hypotheses
- Breakdown: the rise in park requests
- Outliers and anomalies
- Example: the April spike
- Conclusions for your manager
- Example: a half-page conclusion
- Forecasts: handle with care
- Example: an honest forecast
What's inside
- Four PDF versions of the course: for ChatGPT, Gemini, Claude and Microsoft Copilot — each explains where to find the features you need in that assistant.
- 13 ready-to-use prompts and a prompt library you can copy from.
- Step-by-step tasks, before-and-after examples and a one-page cheat sheet.
- Free updates when the AI assistants change.
Who it's for
Office workers, managers, teachers, freelancers and small-business owners who want practical results from AI without technical knowledge.
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