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Data Cleaning with Power BI

You're reading from   Data Cleaning with Power BI The definitive guide to transforming dirty data into actionable insights

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Product type Paperback
Published in Feb 2024
Publisher
ISBN-13 9781805126409
Length 340 pages
Edition 1st Edition
Languages
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Author (1):
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Gus Frazer Gus Frazer
Author Profile Icon Gus Frazer
Gus Frazer
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Toc

Table of Contents (23) Chapters Close

Preface 1. Part 1 – Introduction and Fundamentals
2. Chapter 1: Introduction to Power BI Data Cleaning FREE CHAPTER 3. Chapter 2: Understanding Data Quality and Why Data Cleaning is Important 4. Chapter 3: Data Cleaning Fundamentals and Principles 5. Chapter 4: The Most Common Data Cleaning Operations 6. Part 2 – Data Import and Query Editor
7. Chapter 5: Importing Data into Power BI 8. Chapter 6: Cleaning Data with Query Editor 9. Chapter 7: Transforming Data with the M Language 10. Chapter 8: Using Data Profiling for Exploratory Data Analysis (EDA) 11. Part 3 – Advanced Data Cleaning and Optimizations
12. Chapter 9: Advanced Data Cleaning Techniques 13. Chapter 10: Creating Custom Functions in Power Query 14. Chapter 11: M Query Optimization 15. Chapter 12: Data Modeling and Managing Relationships 16. Part 4 – Paginated Reports, Automations, and OpenAI
17. Chapter 13: Preparing Data for Paginated Reporting 18. Chapter 14: Automating Data Cleaning Tasks with Power Automate 19. Chapter 15: Making Life Easier with OpenAI 20. Assessments 21. Index 22. Other Books You May Enjoy

Questions

  1. What is one of the concerns (described in this chapter) when importing data into Power BI?
    1. Location
    2. Size of data
    3. Data completeness
    4. Formatting
  2. Which Power BI tool can be used to define rules and highlight data points that fall outside of the predefined accuracy ranges?
    1. Power Query
    2. Power Pivot
    3. Conditional formatting
    4. Data profiling
  3. What does Power BI’s DAX language allow users to create?
    1. Visualizations
    2. Semantic models
    3. Relationships between tables
    4. Calculated columns and measures
  4. How can users filter out irrelevant data during the data import process in Power BI?
    1. Using DAX expressions
    2. Through conditional formatting
    3. By creating relationships
    4. Utilizing data transformation capabilities
  5. What is the fundamental concept introduced in the last section?
    1. Data profiling
    2. Data transformation
    3. Data normalization
    4. Data completeness
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