A recurring task from your job, redone with your AI assistant in four blocks: you frame it, you do it, you examine it with your own cases and you present it with its limits and your signature. The project course that closes the Artificial Intelligence learning path for professionals.

An analysis of your own from start to finish: framing it, doing it with an AI copilot, verifying it by another route and explaining it to whoever decides. The project course that closes the data analytics with a copilot learning path.

What happens between typing a question and the answer appearing: word fragments, a map of meanings, attention, training and word-by-word generation. No formulas, with small numbers and one case: a very well-written clause that was not in the policy.

Moving from asking to briefing: how to write a complete brief for an AI assistant, give it just the right context and documents, and demand answers with a source that can be checked.

The risks of using AI at work, explained through the mechanism behind each one and with a method: what can fail, whom it affects, how likely and how serious it is and which control it calls for. A course common to both Data, Analytics and AI learning paths.

What an AI agent can do and with which permissions, how to assign it a long task, how to read what it has done and how much autonomy to give it. You do not build agents: you learn to assign them work and to answer for it.

A company's data lives in tables and is questioned with queries. Learn to read a query you did not write, to commission one from your AI copilot and to check that it counts what it should count, starting with the error that inflates the most figures: the join that duplicates rows.

Read with a critical eye the charts, indicators and reports you are given, more and more of them made by an AI, and present your own honestly: what they show, what they hide and what you sign. A course common to both Data, Analytics and AI learning paths.

How to know whether an AI assistant or agent is fit for a specific task before entrusting it with the task, and how to keep knowing it as it changes: a success criterion, your own battery of cases, someone to grade and a way to tell a real improvement from noise. From the Artificial Intelligence learning path for professionals.

The statistics you need in order not to claim too much: look at the data before summarizing it, record what you clean, give every figure with its margin and know whether a difference is real. Your copilot writes the code; you provide the judgment.

What it means for a machine to learn from data, seen through four models that can be read: multiple regression, time-series forecasting, logistic regression and trees. Your copilot writes the code; you read the model, check it and answer for it.

How to know how far a model can be trusted: test it without tricks, choose the measure by what each error costs, compare it with a simple rule and say what it should not be used for. From the Data Analytics with an AI Copilot learning path.