Transform and load data, define semantic model relationships and calculations, create interactive visuals, and distribute reports using Power BI.

Prerequisites

Completion of the Get started with Microsoft data analytics is recommended.

Modules in this learning path

Get data in Power BI

You'll learn how to retrieve data from a variety of data sources, including Microsoft Excel, relational databases, and NoSQL data stores. You'll also learn how to improve performance while retrieving data.

• Introduction
• Get data from files
• Get data from relational data sources
• Create dynamic reports with parameters
• Get data from a NoSQL database
• Get data from online services
• Select a storage mode
• Get data from Azure Analysis Services
• Fix performance issues
• Resolve data import errors
• Exercise - Prepare data in Power BI Desktop
• Check your knowledge
• Summary

Clean, transform, and load data in Power BI

Power Query has an incredible number of features that are dedicated to helping you clean and prepare your data for analysis. You'll learn how to simplify a complicated model, change data types, rename objects, and pivot data. You'll also learn how to profile columns so that you know which columns have the valuable data that you’re seeking for deeper analytics.

• Introduction
• Shape the initial data
• Simplify the data structure
• Evaluate and change column data types
• Combine multiple tables into a single table
• Profile data in Power BI
• Use Advanced Editor to modify M code
• Exercise - Load data in Power BI Desktop
• Check your knowledge
• Summary

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. A star schema is one way to simplify a semantic model, and you learn about the terminology and implementation of them in this module. You will also learn about why choosing the correct data granularity is important for performance and usability of your Power BI reports. Finally, you learn about improving performance with your Power BI semantic models.

• Introduction
• Work with tables
• Create a date table
• Work with dimensions
• Define data granularity
• Work with relationships and cardinality
• Resolve modeling challenges
• Exercise - Model data in Power BI Desktop
• Check your knowledge
• Summary

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. Additionally, you'll learn about the similarities of, and differences between, a calculated column and a measure.

• Introduction
• Create simple measures
• Create compound measures
• Create quick measures
• Compare calculated columns with measures
• Check your knowledge
• Exercise - Create DAX Calculations in Power BI Desktop
• Summary

Add calculated tables and columns to Power BI Desktop models

By the end of this module, you'll be able to add calculated tables and calculated columns to your semantic model. You'll also be able to 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.

• Introduction
• Create calculated columns
• Learn about row context
• Choose a technique to add a column
• Check your knowledge
• Summary

Design Power BI reports

Because Power BI includes more than 30 core visuals, it can be challenging for a beginner to select the correct visual. This module will guide you through selecting the most appropriate visual type to meet your design and report layout requirements.

• Introduction
• Design the analytical report layout
• Design visually appealing reports
• Report objects
• Select report visuals
• Select report visuals to suit the report layout
• Format and configure visualizations
• Work with key performance indicators
• Exercise - Design a report in Power BI desktop
• Check your knowledge
• Summary

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. Some filtering techniques apply at design time, while others are relevant at report consumption time (in reading view). What matters is that your report design allows report consumers to intuitively narrow down to the data points that interest them.

• Introduction to designing reports for filtering
• Apply filters to the report structure
• Apply filters with slicers
• Design reports with advanced filtering techniques
• Consumption-time filtering
• Select report filter techniques
• Case study - Configure report filters based on feedback
• Check your knowledge
• Summary

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.

• Introduction
• Distribute a report or dashboard
• Monitor usage and performance
• Recommend a development life cycle strategy
• Troubleshoot data by viewing its lineage
• Configure data protection
• Check your knowledge
• Summary

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.

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