Advanced Analytics in Power BI with R and Python: Ingesting, Transforming, Visualizing
von
Ryan Wade
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Science & Technology
Business & Economics
Format
Kindle
Seiten
450
Sprache
Englisch
Veröffentlicht
Jan 1, 2020
Verlag
Apress
Ausgabe
1st ed.
ISBN-10
1484258290
ISBN-13
9781484258293
Beschreibung
Ryan Wade offers a comprehensive resource for professionals eager to harness the power of R and Python within Power BI. This guide stands out as a practical tool, packed with clear, step-by-step recipes that simplify the complexities of data analytics. Readers will find themselves immersed in techniques for ingesting, transforming, and visualizing data, gaining insights that drive business decisions and inform strategies.
With a focus on hands-on applications, the book bridges the gap between programming and real-world data analysis. Each chapter unfolds essential concepts, empowering readers to integrate advanced analytics seamlessly into their workflows. From basic functions to more sophisticated models, readers will discover how to leverage the strengths of both R and Python to enrich their data stories.
Wade’s approachable writing style makes the content accessible for both novice and experienced data analysts. As readers progress, they will build confidence in using these powerful programming languages alongside Power BI, ultimately unlocking new potentials for data-driven decision-making.
With a focus on hands-on applications, the book bridges the gap between programming and real-world data analysis. Each chapter unfolds essential concepts, empowering readers to integrate advanced analytics seamlessly into their workflows. From basic functions to more sophisticated models, readers will discover how to leverage the strengths of both R and Python to enrich their data stories.
Wade’s approachable writing style makes the content accessible for both novice and experienced data analysts. As readers progress, they will build confidence in using these powerful programming languages alongside Power BI, ultimately unlocking new potentials for data-driven decision-making.
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