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Microsoft Power BI Data Analyst Certification Guide

You're reading from   Microsoft Power BI Data Analyst Certification Guide A comprehensive guide to becoming a confident and certified Power BI professional

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781803238562
Length 398 pages
Edition 1st Edition
Languages
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Authors (2):
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Edward Corcoran Edward Corcoran
Author Profile Icon Edward Corcoran
Edward Corcoran
Orrin Edenfield Orrin Edenfield
Author Profile Icon Orrin Edenfield
Orrin Edenfield
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Table of Contents (25) Chapters Close

Preface 1. Part 1 – Preparing the Data
2. Chapter 1: Overview of Power BI and the PL-300 Exam FREE CHAPTER 3. Chapter 2: Connecting to Data Sources 4. Chapter 3: Profiling the Data 5. Chapter 4: Cleansing, Transforming, and Shaping Data 6. Part 2 – Modeling the Data
7. Chapter 5: Designing a Data Model 8. Chapter 6: Using Data Model Advanced Features 9. Chapter 7: Creating Measures Using DAX 10. Chapter 8: Optimizing Model Performance 11. Part 3 – Visualizing the Data
12. Chapter 9: Creating Reports 13. Chapter 10: Creating Dashboards 14. Chapter 11: Enhancing Reports 15. Part 4 – Analyzing the Data
16. Chapter 12: Exposing Insights from Data 17. Chapter 13: Performing Advanced Analysis 18. Part 5 – Deploying and Maintaining Deliverables
19. Chapter 14: Managing Workspaces 20. Chapter 15: Managing Datasets 21. Part 6 – Practice Exams
22. Chapter 16: Practice Exams 23. Other Books You May Enjoy Appendix: Practice Question Answers

Using anomaly detection

One of the great ways to find outliers is using the built-in Find anomalies tool in the Analysis pane of a visualization in Power BI. This feature of Power BI uses Artificial Intelligence (AI) capabilities to identify which data points are most different from other values in your data. The purpose of this feature is to help with analyzing the data so that action can be taken either against the data before business decisions are made or to help report consumers better understand the data and make informed business decisions.

To use this capability, you need to be using a supported visual (such as a line or scatter chart) and only one field on the y axis or values. This feature is being improved; so, at the time of writing, this was the requirement.

Figure 13.2 – Automated anomaly detection is built into some visualizations

Once this has been enabled, you can see the outliers identified with a gray dot. These are anomalies that...

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