Self-service AI with Power BI Desktop : machine learning insights for business /

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 avail...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ehrenmueller-Jensen, Markus
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress L.P., 2020.
Temas:
Microsoft .NET Framework.
Business > Data processing.
Artificial intelligence.
Machine learning.
Computer science.
Artificial Intelligence
Electronic Data Processing
Gestion > Informatique.
Intelligence artificielle.
Apprentissage automatique.
Informatique.
artificial intelligence.
Microsoft programming.
Computers > Intelligence (AI) & Semantics.
Computers > Computer Science.
Computers > Programming > Microsoft Programming.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Author
  • About the Technical Reviewer
  • Acknowledgments
  • Introduction
  • Power BI
  • Power BI Desktop
  • Installing Power BI Desktop
  • Power BI Service
  • Power BI Report Server
  • Power BI Mobile App
  • Self-Service BI vs. Enterprise BI
  • A New Release Every Few Weeks
  • Artificial Intelligence and All That Jazz
  • Chapter Overview
  • Example Database
  • Example Reports
  • Chapter 1: Asking Questions in Natural Language
  • Q & A Visual
  • How to Create a Q & A Visual
  • Q & A Visual Applied
  • Q & A Button
  • Q & A Dialog
  • Keywords
  • Synonyms
  • Teach Q & A
  • Linguistic Schema
  • Key Takeaways
  • Chapter 2: The Insights Feature
  • Explain the Increase
  • Explain the Decrease
  • Find Different Distributions
  • Types of Insights
  • Quick Insights Feature
  • Types of Quick Insights
  • Key Takeaways
  • Chapter 3: Discovering Key Influencers
  • Introduction
  • Analyze Categorical Data
  • Analyze Continuous Data
  • Explain by Categorical Data
  • Explain by Continuous Data
  • Setting Granularity
  • Filters
  • Top Segments
  • Top Segments Detail
  • Types of Influences
  • Fields
  • Format Options
  • Data Model
  • Key Takeaways
  • Chapter 4: Drilling Down and Decomposing Hierarchies
  • Expand and Collapse in a Visual
  • Drilling Up and Down in a Visual
  • Hierarchies in the Data Model
  • Drill-through
  • Drill-through for a Measure
  • Drill-through for a Column
  • Drill-through to a Different Report
  • Tooltip
  • Decomposition Tree (traditional)
  • Decomposition Tree (smart)
  • Key Takeaways
  • Chapter 5: Adding Smart Visualizations
  • Trendline
  • Trendline in DAX
  • Forecast
  • Adding a Custom Visualization
  • Time Series Forecasting Chart
  • Forecasting with ARIMA
  • Forecasting TBATS
  • Time Series Decomposition Chart
  • Scatter Chart with Trendline
  • Spline Chart
  • Clustering
  • Clustering with Outliers
  • Outliers Detection
  • Correlation Plot
  • Decision Tree Chart
  • Word Cloud
  • Key Takeaways
  • Chapter 6: Experimenting with Scenarios
  • Scenarios in Action
  • Creating a What-if Parameter
  • Creating Measures in DAX
  • Ahead of the Curve
  • DAX at Its Best
  • Key Takeaways
  • Chapter 7: Characterizing a Dataset
  • Power Query
  • Column Quality
  • Column Distribution
  • Quality and Distribution Peek
  • Column Profile
  • Table Profile
  • Key Takeaways
  • Chapter 8: Creating Columns from Examples
  • Power Query Mashup Language
  • Add a Custom Column
  • Column from Examples
  • Web Scraping
  • Web by Example
  • Key Takeaways
  • Chapter 9: Executing R and Python Visualizations
  • R and Python
  • Getting Power BI Ready for R
  • Getting Power BI Ready for Python
  • Introduction to R and Python Visualizations
  • Simple R Script Visual
  • Simple Python Script Visual
  • R Script Editor and Python Script Editor
  • R Script Visual: Table
  • R Script Visual: Trendline
  • R Script Visual: ARIMA
  • R Script Visual: Time-Series Decomposition
  • R Script Visual: Scatter with Trendline

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