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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

Grouping and binning

Grouping and binning are techniques used by analysts and statisticians to better understand data and draw insights from it. For example, you might sell products that have various sizes, such as extra small, small, medium, large, extra large, and 2X large. When you look at the sales data, the number of extra small, extra large, and 2X large sales might be much less than the other categories of products. So, in cases such as that, you might want to redefine the categories into small, medium, and large, grouping small and extra small together and large, extra large, and 2X large together to best understand your sales patterns by aggregating the data first. The same technique can be used for numeric or date types; however, in those cases, it's typically referred to as binning. For example, you may have sales data that is daily, but you want to understand long-term patterns, so you bin the data together by aggregating the daily data into weekly or monthly bins...

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