Course overview

Skills measured

• Prepare the data (15–20%)

•Model the data (30–35%)

• Visualize and analyze the data (30–35%)

• Deploy and maintain assets (10–15%)


Prepare the data (15–20%)

Get data from different data sources

• Identify and connect to a data source
•Change data source settings
• Select a shared dataset or create a local dataset
•Select a storage mode
• Use Microsoft Dataverse
•Change the value in a parameter
• Connect to a data flowClean, transform, and load the data
• Profile the data•Resolve inconsistencies, unexpected or null values, and data quality issues
•Identify and create appropriate keys for joins
•Evaluate and transform column data types
• Shape and transform tables
• Combine queries
•Apply user-friendly naming conventions to columns and queries
• Configure data loading
•Resolve data import errors


Model the data (30–35%)

Design a data model

• Define the tables
• Configure table and column properties
• Design and implement role-playing dimensions
• Define a relationship’s cardinality and cross-filter direction
• Design a data model that uses a star schema
•Create a common date table Develop a data model
• Create calculated tables
• Create hierarchies
• Create calculated columns
• Implement row-level security roles
• Use the Q&A feature