Power BI, from raw data to decisions- Power Query, star schema, dashboards
Our Power BI training is designed for teams with mixed skill levels, with one clear goal: becoming autonomous across the full Power BI workflow, from raw data to publishing reliable, usable reports.
In practice, participants learn to:
We don’t believe in trainings that stack feature after feature. We focus on structured immersion, guided by a scenario-based throughline: participants learn the Power BI flow in the right order and develop habits that last.
Our approach follows a progressive logic:
1) Use Power BI like an advanced end user
Before building, participants learn how to work with a report properly: navigation, filters, interactions, drill-down/drill-through, and interpreting visuals and KPIs. The goal is to understand what a good report must enable—and how users actually interact with information.
2) DAX & KPI design
Starting from a clean, pre-built model, participants learn the right DAX fundamentals: measures vs columns, essential functions, and KPI logic. The goal is to create KPIs that are reliable, readable, and reusable.
3) Data visualization & decision-ready reporting
Once KPIs are in place, participants focus on data visualization: page structure, visual selection, report design, and interactivity (filters, buttons, bookmarks, drill-through). They learn how to deliver reporting that is clear, consistent, and adopted.
4) Power Query & modeling (data architecture)
Finally, we go back “to the start”: unprepared data, Power Query transformations, then modeling (star schema, relationships, cardinality, filter direction) to build a model that is performant and maintainable.
Throughout the training, participants validate what they’ve learned through short explain-back moments and guided reviews—so the goal is real, transferable understanding, not just “having seen it once.”
General introduction
Getting started with Power BI and becoming familiar with the different environments:
Learning how to enrich analysis and visuals with DAX:
Understanding how data is extracted, why it must be prepared, and how to transform it:
Hands-on data preparation: