Extending Power BI with Python and R: Perform advanced analysis using the power of analytical languages

by Luca Zavarella, Rajat Talwar (Foreword by)

Overview

The latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python.

This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis.

You'll reinforce your learning with questions at the end of each chapter.

Product Details

ISBN-13:	9781837635863
Publisher:	Packt Publishing
Publication date:	03/29/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	814
File size:	75 MB
Note:	This product may take a few minutes to download.

About the Author

Luca Zavarella has a rich background as an Azure Data Scientist Associate and Microsoft MVP, with a Computer Engineering degree from the University of L'Aquila. His decade-plus experience spans the Microsoft Data Platform, starting as a T-SQL developer on SQL Server 2000 and 2005, then mastering the full suite of Microsoft Business Intelligence tools (SSIS, SSAS, SSRS), and advancing into data warehousing. Recently, his focus has shifted to advanced analytics, data science, and AI, contributing to the community as a speaker and blogger, especially on Medium. Currently, he leads the Data & AI division at iCubed, and he also holds an honors degree in classical piano from the "Alfredo Casella" Conservatory in L'Aquila.

Table of Contents

Table of Contents

1. Where and How to Use R and Python Scripts in Power BI
2. Configuring R with Power BI
3. Configuring Python with Power BI
4. Solving Common Issues When Using Python and R in Power BI
5. Importing Unhandled Data Objects
6. Using Regular Expressions in Power BI
7. Anonymizing and Pseudonymizing your Data in Power BI
8. Logging Data from Power BI to External Sources
9. Loading Large Datasets Also Beyond the Available RAM in Power BI
10. Boosting Data Loading Speed in Power BI with Parquet Format
11. Calling External APIs To Enrich Your Data
12. Calculating Columns Using Complex Algorithms: Distances
13. Calculating Columns Using Complex Algorithms: Fuzzy Matching
14. Calculating Columns Using Complex Algorithms: Optimization Problems
15. Adding Statistics Insights: Associations
16. Adding Statistics Insights: Outliers and Missing Values
17. Using Machine Learning Without Premium or Embedded Capacity
18. Using SQL Server External Languages for Advanced Analytics and ML Integration in Power BI
19. Exploratory Data Analysis
20. Using the Grammar of Graphics in Python with plotnine
21. Advanced Visualizations
22. Interactive R Custom Visuals