Creating a great data model is one of the most important tasks that a data analyst can perform in Microsoft Power BI. The “Model Data in Microsoft Power BI” course teaches you how to create effective data models using DAX language (data analytics expressions) as well as other desktop features to build analytics solutions and improve performance using Microsoft Power BI query data retrieval tasks.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser.
- Designing a data model in Power BI
- Understanding dimensions, defining data granularity, and working with relationships and cardinality
- Creating measures using Data Analysis Expressions (DAX) in Power BI
- Optimizing a model for performance in Power BI
Model Data in Power BI [6 lessons]
Design a Data Model in Power BI - I 2hLesson Objectives
- Design a data model in Power BI
- Create a common date table
- Configure table and column properties
Design a Data Model in Power BI — II 2hLesson Objectives
- Configure many-to-many relationships
- Resolve circular relationships
- Define data granularity
- Design a star schema
Creating Measures Using Data Analysis Expressions-DAX 2hLesson Objectives
- Create calculated columns
- Use DAX to build measures
- Use the CALCULATE function to manipulate filters
- Implement time intelligence by using DAX
Optimize a Model for Performance in Power BI - I 1hLesson Objectives
- Review the performance of measures, relationships, and visuals
- Use variables to improve performance and troubleshooting
- Optimize performance and reduce the size of the model
Optimize a Model for Performance in Power BI — II 1hLesson Objectives
- Improve performance by reducing cardinality levels
- Optimize models with table level storage
- Create and manage aggregations
Guided Project: Modeling Data in Power BI 1hLesson Objectives
- Apply data cleaning and transformation in real-world situations
- Apply modeling data to real-world situations
Projects in this course
Guided Project: Modeling Data in Power BI
Apply data cleaning and data model building to real-world scenarios
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