Data Cleaning with Power BI (eBook)
340 Seiten
Packt Publishing (Verlag)
978-1-80512-605-8 (ISBN)
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.
Unlock the full potential of your data by mastering the art of cleaning, preparing, and transforming data with Power BI for smarter insights and data visualizationsKey FeaturesImplement best practices for connecting, preparing, cleaning, and analyzing multiple sources of data using Power BIConduct exploratory data analysis (EDA) using DAX, PowerQuery, and the M languageApply your newfound knowledge to tackle common data challenges for visualizations in Power BIPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMicrosoft 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.What you will learnConnect to data sources using both import and DirectQuery optionsUse the Query Editor to apply data transformationsTransform your data using the M query languageDesign clean and optimized data models by creating relationships and DAX calculationsPerform exploratory data analysis using Power BIAddress the most common data challenges with best practicesExplore the benefits of using OpenAI, ChatGPT, and Microsoft Copilot for simplifying data cleaningWho this book is forIf you re a data analyst, business intelligence professional, business analyst, data scientist, or anyone who works with data on a regular basis, this book is for you. It s a useful resource for anyone who wants to gain a deeper understanding of data quality issues and best practices for data cleaning in Power BI. If you have a basic knowledge of BI tools and concepts, this book will help you advance your skills in Power BI.]]>
| Erscheint lt. Verlag | 29.2.2024 |
|---|---|
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik |
| ISBN-10 | 1-80512-605-9 / 1805126059 |
| ISBN-13 | 978-1-80512-605-8 / 9781805126058 |
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