Markus Ehrenmueller-Jensen

Self-Service AI with Power BI Desktop

Machine Learning Insights for Business

• Broschiertes Buch

Produktbeschreibung

This book explains how you can enrich the data you have loaded into Power BI Desktop by accessing a suite of Artificial Intelligence (AI) features. These AI features are built into Power BI Desktop and help you to gain new insights from existing data. Some of the features are automated and are available to you at the click of a button or through writing Data Analysis Expressions (DAX). Other features are available through writing code in either the R, Python, or M languages. This book opens up the entire suite of AI features to you with clear examples showing when they are best applied and how to invoke them on your own datasets.
No matter if you are a business user, analyst, or data scientist - Power BI has AI capabilities tailored to you. This book helps you learn what types of insights Power BI is capable of delivering automatically. You will learn how to integrate and leverage the use of the R and Python languages for statistics, how to integrate with Cognitive Services andAzure Machine Learning Services when loading data, how to explore your data by asking questions in plain English ... and more! There are AI features for discovering your data, characterizing unexplored datasets, and building what-if scenarios.
There's much to like and learn from this book whether you are a newcomer to Power BI or a seasoned user. Power BI Desktop is a freely available tool for visualization and analysis. This book helps you to get the most from that tool by exploiting some of its latest and most advanced features.
What You Will LearnAsk questions in natural language and get answers from your data
Let Power BI explain why a certain data point differs from the restHave Power BI show key influencers over categories of dataAccess artificial intelligence features available in the Azure cloudWalk the same drill down path in different parts of your hierarchyLoad visualizations to add smartness to your reportsSimulate changes in data and immediately see the consequencesKnow your data, even before you build your first reportCreate new columns by giving examples of the data that you needTransform and visualize your data with the help of R and Python scripts
Who This Book Is For
For the enthusiastic Power BI user who wants to apply state-of-the-art artificial intelligence (AI) features to gain new insights from existing data. For end-users and IT professionals who are not shy of jumping into a new world of machine learning and are ready to make that step and take a deeper look into their data. For those wanting to step up their game from doing simple reporting and visualizations by making the move into diagnostic and predictive analysis.

Produktdetails

• Verlag: Apress / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4842-6230-6
• 1st ed.
• Seitenzahl: 368
• Erscheinungstermin: 13. September 2020
• Englisch
• Abmessung: 254mm x 178mm x 20mm
• Gewicht: 692g
• ISBN-13: 9781484262306
• ISBN-10: 1484262301
• Artikelnr.: 59802438

Autorenporträt

¿Markus Ehrenmueller-Jensen is a data professional who started his career as a consultant for Business Intelligence solutions on an IBM AS/400 system before he was introduced into the world of Microsoft's Data Platform in 2006. He has since built Data Warehouses and Business Intelligence solutions for a wide variety of clients. His portfolio includes training and workshops, architectural designs, and the development of data-oriented solutions. In 2018 he founded Savory Data, an independent consultancy company.  Markus is certified as a Microsoft Certified Solution Expert (MCSE) for Data Platform and Business Intelligence, and as a Microsoft Certified Trainer (MCT). He teaches databases, information management, and project management at HTL Leonding, Austria. He co-founded both PASS Austria and PUG Austria and co-organizes the yearly SQL Saturday in Vienna. He has been a Microsoft Most Valuable Professional (MVP) each year since 2017.

Inhaltsangabe

1. Asking Questions in Natural Language.- 2. The Insights Feature.- 3. Discovering Key Influencers.- 4. Drill-Down and Decomposing Hierarchies.- 5. Adding Smart Visualizations.- 6. Experimenting with Scenarios.- 7. Characterizing a Dataset.- 8. Creating Columns from Example.- 9. Executing R and Python Visualizations.- 10. Transforming Data with R and Python.- 11. Execute Machine Learning Models in the Azure Cloud.