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Module 1: Discover data analysis In this module, you will explore the different roles in data and learn the different tasks of a data analyst. After completing this module, students will be able to:

• Describe the roles in data
• Describe the tasks of a data analyst

Module 2: Get started building with Power BI Learn what Power BI is, including its building blocks and how they work together. After completing this module, students will be able to:

• Describe how Power BI services and applications work together
• Explore how Power BI can make your business more efficient
• Create compelling visuals and reports

Module 3: Get data in Power BI You'll learn how to retrieve data from a wide variety of data sources, including Microsoft Excel, relational databases, and NoSQL data stores. You'll also learn how to improve performance while retrieving data. After completing this module, students will be able to:

• 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 You will learn how to simplify a complicated model, change data types, rename objects, and pivot data. You will also learn how to profile columns so that you know which columns have the valuable data that you’re seeking for deeper analytics. After completing this module, students will be able to:

• 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 semantic model in Power BI The process of creating a complicated semantic model in Power BI is straightforward. If your data is coming in from more than one transactional system, before you know it, you can have dozens of tables that you have to work with. Building a great semantic model is about simplifying the disarray. After completing this module, students will be able to:

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

Module 6: Add measures to Power BI Desktop models In this module, you'll learn how to work with implicit and explicit measures. You'll start by creating simple measures, which summarize a single column or table. Then, you'll create more complex measures based on other measures in the model. After completing this module, students will be able to:

• 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 You will learn how to add calculated tables and calculated columns to your semantic model, describe row context which is used to evaluated calculated column formulas. Because it's possible to add columns to a table using Power Query, you'll also learn when it's best to create calculated columns instead of Power Query custom columns. After completing this module, students will be able to:

• 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 By the end of this module, you'll learn the meaning of time intelligence and how to add time intelligence DAX calculations to your model. After completing this module, students will be able to:

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

Module 9: Optimize a model for performance in Power BI Performance optimization, also known as performance tuning, involves making changes to the current state of the semantic model so that it runs more efficiently. Essentially, when your semantic model is optimized, it performs better. After completing this module, students will be able to:

• 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 This module will guide you through selecting the most appropriate visual type to meet your design and report layout requirements. After completing this module, students will be able to:

• 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 Report filtering is a complex topic because many techniques are available for filtering a Microsoft Power BI report. However, with complexity comes control, allowing you to design reports that meet requirements and expectations. After completing this module, students will be able to:

• 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 The features and capabilities that are covered in this module will help you enhance your reports to make them more refined. After completing this module, students will be able to:

• 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 You'll learn how to use Power BI to perform data analytical functions, how to identify outliers in your data, how to group data together, and how to bin data for analysis. You'll also learn how to perform time series analysis. Finally, you'll work with advanced analytic features of Power BI, such as Quick Insights, AI Insights, and the Analyze feature. After completing this module, students will be able to:

• 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 Learn how to navigate the Power BI service, create and manage workspaces and related items, and distribute reports to users. After completing this module, students will be able to:

• 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 semantic models in Power BI With Microsoft Power BI, you can use a single semantic model to build many reports. Reduce your administrative overhead even more with scheduled semantic model refreshes and resolving connectivity errors. After completing this module, students will be able to:

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

Module 16: Create dashboards in Power BI Microsoft Power BI dashboards are different than Power BI reports. Dashboards allow report consumers to create a single artifact of directed data that is personalized just for them. Dashboards can be composed of pinned visuals that are taken from different reports. After completing this module, students will be able to:

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

Module 17: Implement row-level security Row-level security (RLS) allows you to create a single or a set of reports that targets data for a specific user. In this module, you'll learn how to implement RLS by using either a static or dynamic method and how Microsoft Power BI simplifies testing RLS in Power BI Desktop and Power BI service. After completing this module, students will be able to:

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

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