PL-300T00

Successful Data Analysts start this role with experience of working with data in the cloud.

Specifically:

• Understanding core data concepts.
• Knowledge of working with relational data in the cloud.
• Knowledge of working with non-relational data in the cloud.
• Knowledge of data analysis and visualization concepts.

Students will learn to,

• Discover data analysis
• Get started building with Power BI
• Get data in Power BI
• Clean, transform, and load data in Power BI
• Design a data model in Power BI
• Add measures to Power BI Desktop models
• Add calculated tables and columns to Power BI Desktop models
• Use DAX time intelligence functions in Power BI Desktop models
• Optimize a model for performance in Power BI
• Design Power BI reports
• Configure Power BI report filters
• Enhance Power BI report designs for the user experience
• Perform analytics in Power BI
• Create and manage workspaces in Power BI
• Manage datasets in Power BI
• Create dashboards in Power BI
• Implement row-level security

Module 1 :Discover data analysis

• Learn about the roles in data.
• Learn about the tasks of a data analyst.

Module 2 :Get started building with Power BI

• Learn how Power BI services and applications work together.
• Explore how Power BI can make your business more efficient.
• Learn how to create compelling visuals and reports.

Module 3 :Get data in Power BI

• Identify and connect to a data source
• Get data from a relational database, like Microsoft SQL Server
• Get data from a file, like Microsoft Excel
• Get data from applications
• Get data from Azure Analysis Services
• Select a storage mode
• Fix performance issues
• Resolve data import errors

Module 4 :Clean, transform, and load data in Power BI

• Resolve inconsistencies, unexpected or null values, and data quality issues.
• Apply user-friendly value replacements.
• Profile data so you can learn more about a specific column before using it.
• Evaluate and transform column data types.
• Apply data shape transformations to table structures.
• Combine queries.
• Apply user-friendly naming conventions to columns and queries.
• Edit M code in the Advanced Editor.

Module 5 :Design a data model in Power BI

• Create common date tables
• Configure many-to-many relationships
• Resolve circular relationships
• Design star schemas

Module 6 : Add measures to Power BI Desktop models

• Determine when to use implicit and explicit measures.
• Create simple measures.
• Create compound measures.
• Create quick measures.
• Describe similarities of, and differences between, a calculated column and a measure.

Module 7 : Add calculated tables and columns to Power BI Desktop models

• Create calculated tables.
• Create calculated columns.
• Identify row context.
• Determine when to use a calculated column in place of a Power Query custom column.
• Add a date table to your model by using DAX calculations.

Module 8 : Use DAX time intelligence functions in Power BI Desktop models

• Define time intelligence.
• Use common DAX time intelligence functions.
• Create useful intelligence calculations.

Module 9 : Optimize a model for performance in Power BI

• Review the performance of measures, relationships, and visuals.
• Use variables to improve performance and troubleshooting.
• Improve performance by reducing cardinality levels.
• Optimize DirectQuery models with table level storage.
• Create and manage aggregations.

Module 10 : Design Power BI reports

• Learn about the structure of a Power BI report.
• Learn about report objects.
• Select the appropriate visual type to use.

Module 11 : Configure Power BI report filters

• Design reports for filtering.
• Design reports with slicers.
• Design reports by using advanced filtering techniques.
• Apply consumption-time filtering.
• Select appropriate report filtering techniques.

Module 12 : Enhance Power BI report designs for the user experience

• Design reports to show details.
• Design reports to highlight values.
• Design reports that behave like apps.
• Work with bookmarks.
• Design reports for navigation.
• Work with visual headers.
• Design reports with built-in assistance.
• Use specialized visuals.

Module 13 : Perform analytics in Power BI

• Explore statistical summary.
• Identify outliers with Power BI visuals.
• Group and bin data for analysis.
• Apply clustering techniques.
• Conduct time series analysis.
• Use the Analyze feature.
• Use advanced analytics custom visuals.
• Review Quick insights.
• Apply AI Insights.

Module 14 : Create and manage workspaces in Power BI

• Create and manage Power BI workspaces and items.
• Distribute a report or dashboard.
• Monitor usage and performance.
• Recommend a development lifecycle strategy.
• Troubleshoot data by viewing its lineage.
• Configure data protection.

Module 15 : Manage datasets in Power BI

• Use a Power BI gateway to connect to on-premises data sources.
• Configure a scheduled refresh for a dataset.
• Configure incremental refresh settings.
• Manage and promote datasets.
• Troubleshoot service connectivity.
• Boost performance with query caching (Premium).

Module 16 : Create dashboards in Power BI

• Set a mobile view.
• Add a theme to the visuals in your dashboard.
• Configure data classification.
• Add real-time dataset visuals to your dashboards.
• Pin a live report page to a dashboard.

Module 17 : Implement row-level security

• Configure row-level security by using a static method.
• Configure row-level security by using a dynamic method.

Successful Data Analysts start this role with experience of working with data in the cloud.

Specifically:

• Understanding core data concepts.
• Knowledge of working with relational data in the cloud.
• Knowledge of working with non-relational data in the cloud.
• Knowledge of data analysis and visualization concepts.

Exam PL-300: Microsoft Power BI Data Analyst