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Data Cleaning with Power BI

Data Cleaning with Power BI

By : Frazer
5 (7)
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Data Cleaning with Power BI

Data Cleaning with Power BI

5 (7)
By: Frazer

Overview of this book

Microsoft Power BI offers a range of powerful data cleaning and preparation options through tools such as DAX, Power Query, and the M language. However, despite its user-friendly interface, mastering it can be challenging. Whether you're a seasoned analyst or a novice exploring the potential of Power BI, this comprehensive guide equips you with techniques to transform raw data into a reliable foundation for insightful analysis and visualization. This book serves as a comprehensive guide to data cleaning, starting with data quality, common data challenges, and best practices for handling data. You’ll learn how to import and clean data with Query Editor and transform data using the M query language. As you advance, you’ll explore Power BI’s data modeling capabilities for efficient cleaning and establishing relationships. Later chapters cover best practices for using Power Automate for data cleaning and task automation. Finally, you’ll discover how OpenAI and ChatGPT can make data cleaning in Power BI easier. By the end of the book, you will have a comprehensive understanding of data cleaning concepts, techniques, and how to use Power BI and its tools for effective data preparation.
Table of Contents (23 chapters)
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1
Part 1 – Introduction and Fundamentals
6
Part 2 – Data Import and Query Editor
11
Part 3 – Advanced Data Cleaning and Optimizations
16
Part 4 – Paginated Reports, Automations, and OpenAI

Chapter 4 – The Most Common Data Cleaning Operations

  1. B – To enhance data accuracy in the analysis – Removing duplicates is crucial to prevent inaccuracies in data analysis, especially when dealing with numerical values.
  2. C – Product Name, as the main identifier – In the provided example, the Product Name column is selected to remove duplicates, as it serves as the main identifier.
  3. B – Distorts analysis results – Missing data, or NULL values, can distort analysis results and visuals.
  4. C – To gain desired dimensions for analysis – for example, splitting a date field – Columns may need to be split to extract specific dimensions for analysis.
  5. C – Split Columns by Delimiter, based on data format – In the Date table example, the By Delimiter function is used to split the date column based on the / delimiter.
  6. C – Merging columns to format date data – Merging columns may...

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