Yes, there is hands-on practice, though it's mostly guided exercises rather than a large independent project. You'll work through tasks like configuring a star schema, adding calculated columns and measures, and setting up time intelligence with a common date table. There's also a final applied assessment, so you apply each idea as you learn it and then bring the skills together.

The course focuses on data model design, DAX calculations, and performance improvement in Power BI. You'll work with relationships and schemas, create measures and time-based calculations, and learn how modeling choices affect report behavior. The bigger takeaway is how to organize data so analysis stays accurate and reports stay usable as the model grows.

After finishing, you should be able to build a Power BI model that supports analysis, write core DAX calculations, and troubleshoot some common performance issues. For example, you could connect fact and dimension tables, create measures for revenue or year-over-year comparisons, and adjust model settings to improve report responsiveness. That's a solid scope if you want to move beyond importing data and start shaping it for analysis.

It's more concept-first with guided practice than project-heavy. The course spends time explaining how relationships, DAX, and optimization choices work, then reinforces that through exercises, quizzes, and a final applied task. It suits learners who want to understand the logic behind a model, not just reproduce steps.