This course covers the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will show how to access and process data from a range of data sources including both relational and non-relational sources. Finally, this course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution.
The audience for this course are data professionals and business intelligence professionals who want to learn how to accurately perform data analysis using Power BI. This course is also targeted toward those individuals who develop reports that visualize data from the data platform technologies that exist on both in the cloud and on-premises.
Would you like to explore the journey of a data analyst and learn how a data analyst tells a story with data ? In this module, you explore the different roles in data and learn the different tasks of a data analyst.
Introduction
Overview of data analysis
Roles in data
Tasks of a data analyst
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Understand the crucial role of a data analyst in organizational success.
Recognize how data analysts ensure business decisions are based on reliable data.
Identify various data roles and their collaboration with data analysts to provide valuable insights.
Learn about Power BI, the building blocks and flow of Power BI, and how to create compelling, interactive reports.
Introduction
Use Power BI
Building blocks of Power BI
Tour and use the Power BI service
Knowledge check
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Understand that Power BI provides a complete solution for data analytics, including preparation, visualization, and distribution.
Recognize semantic models and visualizations as key elements of Power BI.
Identify the components of Power BI.
Use Power BI to make data-informed decisions in their organization.
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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Pull data from various sources into Power BI.
Handle data sources appropriately (e.g., Excel tables, SQL Server Analysis Services).
Choose between Import (for quick interactions) and Direct Query (for real-time updates).
Identify and resolve performance issues and data import errors.
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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Simplify complex data for easier use and calculations.
Combine queries to streamline data navigation.
Replace and rename columns with human-readable names.
Apply good naming conventions for objects in Power BI.
In this module, you’ll learn about the Power BI Desktop model structure, star schema design basics, analytics queries, and report visual configuration. This module provides a strong foundation on which you can learn to optimize model designs and add model calculations.
Introduction
Star schema design
Analytic queries
Configure report visuals
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Understand that a Power BI model is queried by report and dashboard visuals.
Recognize the structure of Power BI models, including tables and columns.
Apply star schema design principles, classifying tables as fact or dimension to optimize models for user-friendliness and performance.
Use fields (model columns, hierarchy levels, and measures) to set up Power BI visuals, generating analytic queries for data analysis.
Describe model frameworks, their benefits and limitations, and features to help optimize your Power BI data models.
Introduction
Describe Power BI model fundamentals
Determine when to develop an import model
Determine when to develop a DirectQuery model
Determine when to develop a composite model
Choose a model framework
Knowledge check
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Develop a Power BI model using the Import, DirectQuery, or Composite frameworks.
Understand the benefits and limitations of each framework to optimize model performance.
Strive to create a model that delivers fast performance and low latency, even with high-volume data sources.
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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Model data in Power BI, including creating common date tables and configuring many-to-many relationships.
Resolve circular relationships and design star schemas.
Use these skills to build effective visuals and collaborate with other teams.
Explore and utilize the nuances of the semantic model.
In this module, you’ll learn how to write DAX formulas to create calculated tables, calculated columns, and measures, which are different types of model calculations. Additionally, you’ll learn how to write and format DAX formulas, which consist of expressions that use functions, operators, references to model objects, constants, and variables.
Introduction
Write DAX formulas
DAX data types
Work with DAX functions
Use DAX operators
Use DAX variables
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Enhance their Power BI model using DAX with calculations such as calculated tables, columns, and measures.
Apply DAX fundamentals to write accurate and efficient formulas.
Understand DAX data types, functions, operators, and how to reference model objects and constants.
Use DAX variables effectively in their formulas.
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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Distinguish between implicit and explicit measures in Power BI.
Create explicit measures using DAX formulas to summarize model data.
Understand the importance of explicit measures for precise calculations in report visuals.
Recognize that more advanced measure creation, including filter modification and iterator functions, will be covered in later modules.
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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Use DAX to extend model designs with calculated tables and calculated columns.
Understand that calculated columns are evaluated within row context and reference columns from different tables correctly.
Recognize that Power Query custom columns are generally preferred for adding new columns to your model.
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.
Introduction
Use DAX time intelligence functions
Additional time intelligence calculations
Exercise – Create Advanced DAX Calculations in Power BI Desktop
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Use DAX time intelligence functions to modify the filter context for date filters.
Create calculations such as year-to-date (YTD) and year-over-year (YoY) using time intelligence functions.
Perform life-to-date (LTD) calculations to count new occurrences in fact data.
Filter snapshot data to ensure that only a single snapshot value is returned.
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.
Introduction to performance optimization
Review performance of measures, relationships, and visuals
Use variables to improve performance and troubleshooting
Reduce cardinality
Optimize DirectQuery models with table level storage
Create and manage aggregations
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Identify and address performance issues in Power BI Desktop semantic models.
Use tools like Performance Analyzer to review and optimize the performance of measures, relationships, and visuals.
Implement variables to simplify and improve the efficiency of calculations.
Reduce column cardinality and optimize relationships to enhance model performance.
Consider and apply optimizations for DirectQuery models and source databases.
Use aggregations to significantly reduce the size of the semantic model.
Understand the benefits of these optimizations for improving report performance, user satisfaction, and resource management.
Enforce model security in Power BI using row-level security and object-level security.
Introduction
Restrict access to Power BI model data
Restrict access to Power BI model objects
Apply good modeling practices
Exercise: Enforce model security
Knowledge check
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Implement Row-Level Security (RLS) to restrict access to subsets of model data, tables, or columns.
Understand how DirectQuery models can leverage data source RLS when single sign-on (SSO) is supported, reducing the need for model roles.
Apply best development practices to ensure accurate and efficient enforcement of data permissions.
Gathering appropriate inputs to scope your report design requirements involves identifying your audience, determining the suitable report types, and defining their interface and experience requirements. This module provides you with a strong foundation on which to learn how to plan your report design requirements.
Introduction
Identify the audience
Determine report types
Define user interface requirements
Define user experience requirements
Explore report designs
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Scope report design requirements effectively by understanding business goals and audience needs.
Select the appropriate report type to meet design objectives.
Consider user interface requirements to ensure a user-friendly reporting solution.
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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Design Power BI reports by effectively laying out objects across report pages.
Select the appropriate visual types to clearly communicate data to report consumers.
Overlay analytics options, arrange visual elements intuitively, and apply suitable formatting and styling to enhance report clarity and effectiveness.
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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Understand and apply filters in Power BI reports using the Filters pane and slicers.
Avoid using both filtering techniques simultaneously in the same report to prevent confusion.
Provide demonstrations or videos to show report consumers how to use filters and interactive features such as include/exclude groups, cross-filtering, report page drillthrough, and bookmarks.
The features and capabilities that are covered in this module will help you enhance your reports to make them more refined.
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
Tune report performance
Optimize reports for mobile use
Exercise – Enhance Power BI reports
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Enhance report designs with rich, compelling features that elevate user experience.
Provide instructions or video recordings to guide report consumers on how to interact with the report.
Survey report consumers to gather feedback on what works well and identify areas for improvement.
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.
Introduction to analytics
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
Create what-if parameters
Use specialized visuals
Exercise – Perform Advanced Analytics with AI Visuals
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Leverage advanced analytics in Power BI to enhance decision-making and strategy formulation.
Improve reports and dashboards to enable deeper exploration and more specific insights for various teams.
Conduct statistical summaries to identify key takeaways, trends, and outliers.
Perform time series analysis to observe data progression and forecast future behavior.
Use the Analyze feature to explain data increases and highlight distribution differences.
Incorporate custom visuals to address additional business problems and present data in dynamic ways.
Discover quick insights and apply grouping, binning, and clustering techniques to segment and present data effectively.
Use advanced analytics to uncover new patterns and trends, empowering more informed business decisions and forecasts.
Paginated reports allow report developers to create Power BI artifacts that have tightly controlled rendering requirements. Paginated reports are ideal for creating sales invoices, receipts, purchase orders, and tabular data. This module will teach you how to create reports, add parameters, and work with tables and charts in paginated reports.
Introduction to paginated reports
Get data
Create a paginated report
Work with charts on the report
Publish the report
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Create a Power BI paginated report by connecting to a data source and extracting data into a dataset.
Populate tables or charts with the extracted data.
Format the report to enhance its visual appeal and informativeness.
Publish the finished report to 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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Understand the role of Power BI workspaces in sharing reports, building dashboards, and collaborating with teams.
Utilize workspaces to improve report performance, apply security requirements, and facilitate content sharing.
Manage workspaces in Power BI service to build and maintain dashboards efficiently.
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
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Use two data refresh options to automate and streamline the refresh process for semantic models.
Apply endorsement features to highlight critical semantic models and guide users to the appropriate resources.
Manage and troubleshoot connectivity issues using the on-premises gateway.
Enhance access and update frequency of semantic models to support high-quality report and dashboard creation, facilitating real-time decision-making.
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. Where a Power BI report uses data from a single semantic model, a Power BI dashboard can contain visuals from different semantic models.
Introduction to dashboards
Configure data alerts
Explore data by asking questions
Review Quick insights
Add a dashboard theme
Pin a live report page to a dashboard
Configure a real-time dashboard
Set mobile view
Exercise – Create a Power BI dashboard
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Understand the purpose of dashboards, including tiles and pinning.
Set mobile view for dashboards.
Add themes to dashboard visuals.
Configure data classification settings.
Add real-time semantic model visuals to dashboards.
Pin live report pages to dashboards.
Use dashboards to visualize and tell a story with data.
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.
Introduction
Configure row-level security with the static method
Configure row-level security with the dynamic method
Exercise – Enforce row-level security in Power BI
Check your knowledge
Summary
Lab experience avaible only via Authorized Lab Hoster
Lab experience avaible only via Authorized Lab Hoster
Understand and implement row-level security (RLS) in Power BI to limit data visibility based on user roles.
Create and use DAX formulas to restrict data access, such as filtering by location or transaction amount.
Deploy reports with RLS settings and assign users to specific security roles.
Leverage RLS as a versatile and powerful security feature in Power BI.
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