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 Duration 21 hours

Course Outline

Module 1: Foundations of Data Analysis

Participants will examine the distinct functional roles within data ecosystems and acquire proficiency in the core duties associated with data analyst positions.

Lessons

  • Introduction
  • Overview of data analysis
  • Roles in data
  • Tasks of a data analyst
  • Check your knowledge
  • Summary

Learning objectives

Upon completion of this module, learners will be able to:

  • Identify the various roles within data operations.
  • Describe the primary responsibilities of a data analyst.

Module 2: Introduction to Power BI Development

This module defines the Power BI platform, detailing its constituent components and their integrated functions.

Completion of this module supports preparation for Exam PL-100: Microsoft Power Platform App Maker.

Learning objectives

Upon completion of this module, learners will be able to:

  • Explain the interoperability of Power BI services and applications.
  • Analyze how Power BI can enhance organizational operational efficiency.
  • Demonstrate the creation of effective visuals and reports.

Lessons

  • Introduction
  • Use Power BI
  • Building blocks of Power BI
  • Tour and use the Power BI service
  • Knowledge check
  • Summary

Module 3: Data Ingestion in Power BI

In this module, learners will acquire the skills necessary to extract data from diverse sources, including Microsoft Excel, relational databases, and NoSQL repositories. Additionally, techniques for optimizing data retrieval performance will be addressed.

Learning objectives

By the conclusion of this module, participants will be able to:

  • Identify and establish connections to data sources
  • Extract data from relational databases, such as Microsoft SQL Server
  • Import data from files, including Microsoft Excel
  • Retrieve data from applications
  • Obtain data from Azure Analysis Services
  • Select an appropriate storage mode
  • Diagnose and resolve performance bottlenecks
  • Correct data import errors

Lessons

  • 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

Module 4: Data Cleaning, Transformation, and Loading in Power BI

This module instructs participants on simplifying complex models, modifying data types, renaming objects, and pivoting data. It further covers column profiling to isolate valuable datasets for advanced analytical processes.

Learning objectives

By the end of this module, participants will be able to:

  • Address inconsistencies, unexpected values, nulls, and data quality defects.
  • Implement user-centric value substitutions.
  • Profile data to assess column characteristics prior to utilization.
  • Evaluate and transform column data types.
  • Execute data shape transformations on table structures.
  • Merge multiple queries.
  • Apply standardized naming conventions to columns and queries.
  • Edit M code within the Advanced Editor.

Lessons

  • 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

Module 5: Data Model Design in Power BI

While constructing complex data models in Power BI is methodical, multi-source integration can result in extensive table structures. Effective modeling focuses on reducing complexity. This module examines star schema design, including relevant terminology and implementation. It also addresses the importance of data granularity for report performance and usability, as well as strategies for enhancing model efficiency.

Learning objectives

In this module, participants will:

  • Create standard date tables
  • Configure many-to-many relationships
  • Resolve circular relationship dependencies
  • Design star schema structures

Lessons

  • 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

Module 6: Implementing Measures in Power BI Desktop Models

Participants will learn to manage both implicit and explicit measures. The module progresses from creating basic measures summarizing single columns or tables to developing complex measures derived from other model components. It further distinguishes between calculated columns and measures.

Learning objectives

By the end of this module, participants will be able to:

  • Determine the appropriate use of implicit versus explicit measures.
  • Construct simple measures.
  • Develop compound measures.
  • Generate quick measures.
  • Differentiate between calculated columns and measures.

Lessons

  • 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

Module 7: Adding Calculated Tables and Columns to Power BI Desktop Models

Upon completion of this module, participants will be capable of integrating calculated tables and columns into data models. The module explains row context, which is essential for evaluating calculated column formulas. It also clarifies the strategic distinction between using calculated columns and Power Query custom columns.

Learning objectives

By the end of this module, participants will be able to:

  • Generate calculated tables.
  • Develop calculated columns.
  • Identify row context mechanisms.
  • Determine when to utilize calculated columns instead of Power Query custom columns.
  • Insert date tables using DAX calculations.

Lessons

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

Module 8: Applying DAX Time Intelligence Functions in Power BI Desktop Models

This module defines time intelligence and demonstrates the implementation of time intelligence DAX calculations within data models.

Learning objectives

By the end of this module, participants will be able to:

  • Define the concept of time intelligence.
  • Utilize standard DAX time intelligence functions.
  • Construct practical intelligence calculations.

Lessons

  • Introduction
  • Use DAX time intelligence functions
  • Additional time intelligence calculations
  • Exercise - Create Advanced DAX Calculations in Power BI Desktop
  • Check your knowledge
  • Summary

Module 9: Model Performance Optimization in Power BI

Performance optimization, or tuning, entails adjusting the data model structure to enhance execution efficiency. Optimized models deliver superior performance outcomes.

Learning objectives

By the end of this module, participants will be able to:

  • Assess the performance of measures, relationships, and visuals.
  • Leverage variables to enhance performance and aid troubleshooting.
  • Improve efficiency by reducing cardinality levels.
  • Optimize DirectQuery models using table-level storage.
  • Create and manage aggregations.

Lessons

  • 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

Module 10: Power BI Report Design

Given the extensive library of over 30 core visuals in Power BI, selecting appropriate elements can present a challenge for novice users. This module guides participants in selecting visual types that align with design standards and report layout requirements.

Learning objectives

In this module, participants will:

  • Understand the structural components of a Power BI report.
  • Identify report objects and their functions.
  • Select appropriate visual types for specific use cases.

Lessons

  • 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

Module 11: Configuring Power BI Report Filters

Report filtering involves numerous techniques within Microsoft Power BI. While complex, this capability provides granular control to ensure reports meet specific operational requirements. Some techniques are applied during design, while others function during consumption. The objective is to enable users to efficiently isolate relevant data points.

Learning objectives

In this module, participants will:

  • Design reports with filtering capabilities.
  • Implement slicers for report interaction.
  • Apply advanced filtering methodologies.
  • Configure consumption-time filtering.
  • Select appropriate filtering techniques for specific scenarios.

Lessons

  • 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

Module 12: Enhancing Power BI Report User Experience

The capabilities covered in this module enable the refinement of reports to improve usability and presentation quality.

Learning objectives

In this module, participants will:

  • Design reports to display detailed information.
  • Design reports to emphasize key values.
  • Structure reports to function like application interfaces.
  • Utilize bookmarks for navigation.
  • Design reports with navigational enhancements.
  • Manage visual headers.
  • Incorporate built-in assistance features.
  • Implement specialized visuals.

Lessons

  • 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

Module 13: Advanced Analytics in Power BI

Participants will learn to execute data analytical functions in Power BI, including outlier identification, data grouping, binning, and time series analysis. The module also covers advanced features such as Quick Insights, AI Insights, and the Analyze tool.

Learning objectives

In this module, participants will:

  • Examine statistical summaries.
  • Detect outliers using Power BI visuals.
  • Group and bin data for analytical purposes.
  • Apply clustering methods.
  • Perform time series analysis.
  • Utilize the Analyze feature.
  • Employ advanced analytics custom visuals.
  • Review Quick insights.
  • Apply AI Insights.

Lessons

  • 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

Module 14: Workspace Creation and Management in Power BI

This module covers navigation within the Power BI service, the creation and administration of workspaces and associated assets, and the distribution of reports to authorized users.

Learning objectives

In this module, participants will:

  • Create and administer Power BI workspaces and items.
  • Distribute reports or dashboards.
  • Monitor usage metrics and performance.
  • Advise on development lifecycle strategies.
  • Troubleshoot data issues by examining lineage.
  • Configure data protection settings.

Lessons

  • 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

Module 15: Dataset Management in Power BI

Microsoft Power BI enables the use of a single dataset to generate multiple reports. Administrative overhead can be further reduced through scheduled dataset refreshes and the resolution of connectivity errors.

Learning objectives

In this module, participants will:

  • Utilize a Power BI gateway to connect to on-premises data sources.
  • Configure scheduled dataset refreshes.
  • Set incremental refresh parameters.
  • Manage and promote datasets.
  • Troubleshoot service connectivity issues.
  • Enhance performance using query caching (Premium).

Lessons

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

Module 16: Dashboard Creation in Power BI

Microsoft Power BI dashboards differ from reports. Dashboards allow users to curate personalized views of targeted data. They can aggregate pinned visuals from multiple reports, whereas a single report typically relies on one dataset, and a dashboard may integrate visuals from various datasets.

Learning objectives

In this module, participants will:

  • Configure mobile views.
  • Apply themes to dashboard visuals.
  • Set data classification rules.
  • Integrate real-time dataset visuals into dashboards.
  • Pin live report pages to dashboards.

Lessons

  • 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
  • Configure data classification
  • Set mobile view
  • Exercise - Create a Power BI dashboard
  • Check your knowledge
  • Summary

Module 17: Implementing Row-Level Security

Row-level security (RLS) enables the creation of reports that restrict data access for specific users. This module demonstrates RLS implementation using static or dynamic methods and illustrates how Microsoft Power BI facilitates testing within both Power BI Desktop and the Power BI service.

Learning objectives

In this module, participants will:

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

Lessons

  • 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

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