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Full Description
This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services.
The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that.
What You Will Learn
Create advanced data visualizations via R using the ggplot2 package
Ingest data using R and Python to overcome some limitations of Power Query
Apply machine learning models to your data using R and Python without the need of Power BI premium capacity
Incorporate advanced AI in Power BI without the need of Power BI premium capacity via Microsoft Cognitive Services, IBM Watson Natural Language Understanding, and pre-trained models in SQL Server Machine Learning Services
Perform advanced string manipulations not otherwise possible in Power BI using R and Python
Who This Book Is For
Power users, data analysts, and data scientists who want to go beyond Power BI's built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more concise way
Contents
Part I. Creating Custom Data Visualizations using R.- 1. The Grammar of Graphics.- 2. Creating R custom visuals in Power BI using ggplot2.- Part II. Ingesting Data into the Power BI Data Model using R and Python.- 3. Reading CSV Files.- 4. Reading Excel Files.- 5. Reading SQL Server Data.- 6. Reading Data into the Power BI Data Model via an API.- Part III. Transforming Data using R and Python.-7. Advanced String Manipulation and Pattern Matching.- 8. Calculated Columns using R and Python.- Part IV. Machine Learning & AI in Power BI using R and Python.- 9. Applying Machine Learning and AI to your Power BI Data Models.- 10. Productionizing Data Science Models and Data Wrangling Scripts.
