Extending Power BI with Python and R, Second Edition

eBook Details:

• Paperback: 814 pages
• Publisher: WOW! eBook; 2nd edition (March 29, 2024)
• Language: English
• ISBN-10: 1837639531
• ISBN-13: 978-1837639533

eBook Description:

Extending Power BI with Python and R, 2nd Edition: Perform advanced analysis using the power of analytical languages. Ingest, transform, manipulate, and visualize your data beyond Power BI’s capabilities.

The latest Extending Power BI with Python and R, Second Edition of this book delves deeper 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 laptop RAM, employing parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server External Languages 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 PowerBI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. Next, you’ll learn 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 data sets by plotting multiple visual graphs in the process of building a machine-learning model. The Extending Power BI with Python and R, 2nd Edition book will guide you to Utilize external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis.

• Configure optimal integration of Python and R with Power BI
• Perform complex data manipulations not possible by default in Power BI
• Boost Power BI logging and loading large datasets
• Extract insights from your data using algorithms like linear optimization
• Calculate string distances and learn how to use them for probabilistic fuzzy matching
• Handle outliers and missing values for multivariate and time-series data
• Apply Exploratory Data Analysis in Power BI with R
• Learn to use Grammar of Graphics in Python

You’ll also be able to reinforce learning with questions at the end of each chapter of the Extending Power BI with Python and R, Second Edition book.