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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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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 8 – Using Data Profiling for Exploratory Data Analysis (EDA)

  1. B – To summarize data characteristics and gain insights – EDA serves as a pivotal phase in the data analysis workflow, aiming to summarize data characteristics, identify patterns, detect outliers, and gain insights into data structure.
  2. A – Identifying potential outliers – Benefits of a well-carried-out EDA include familiarizing analysts with the dataset, assessing data scope, identifying data quality issues, revealing patterns and trends, and aiding in the selection of appropriate modeling techniques.
  3. C – Column Transformation – Power BI’s data profiling capabilities include the following:
    • Column Quality Assessment
    • Column Distribution Analysis
    • Column Profile Views
  4. B – Within Power Query, open the View tab – Data profile views can be accessed in Power Query by opening Power Query and selecting the View tab.
  5. B – Histograms...

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