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Data Analysis in Power Bi

50,50 €*

Dieses Produkt erscheint am 14. Juni 2026

Produktnummer: 16A46616117
Autor: Robinson, Scott
Themengebiete: Computers - Languages / Programming
Veröffentlichungsdatum: 14.06.2026
EAN: 9781484293706
Sprache: Englisch
Seitenzahl: 315
Produktart: Kartoniert / Broschiert
Verlag: Apress Apress L.P.
Untertitel: Going Deep Into Data Using Visualization
Produktinformationen "Data Analysis in Power Bi"
Welcome to the first book to explore the powerful tools within Power BI that can enhance and improve your analytical data exploration.You know Power BI's reputation as a reporting, dashboarding, and data visualization tool but it might not occur to you that it has great value as a tool for data exploration. This book examines Power BI's data analysis features and shows you, through real-world examples, how Power BI can be a go-to analysis tool for business users in all domains.You will discover that Microsoft's Power BI offers all the number-crunching power of Excel plus versatile and impactful visualization tools that will greatly enhance your discovery process and make it easier to communicate results. You will see that its data analysis expression (DAX) language is far richer and more powerful than Excel's limited (and outdated) MDX; and its data ingestion utility is vastly superior.You will learn how to unearth unexpected trends andhidden correlations that might be elusive in the numbers but will emerge in high relief using visualization, speeding up analysis and making your data analysis far more complete. You will build analysis pages which, after you have completed a particular analysis, can be preserved along with your datasets for later use, and even passed along to others in an organization as "what-if" tools.Hands-on exercises are provided that use downloadable data sources and "starter" configurations of Power BI files for building sample analyses. Downloadable Excel samples of those same exercises are provided for easy comparison.What You Will LearnUnderstand the exploratory methodologyBuild data sets and take a dive into DAXAdd visualization to your analysis processIncorporate R and PythonUse Power BI to extend your work
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