Book Image

Extending Power BI with Python and R

By : Luca Zavarella
Book Image

Extending Power BI with Python and R

By: Luca Zavarella

Overview of this book

Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you’ll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R.
Table of Contents (22 chapters)
Section 1: Best Practices for Using R and Python in Power BI
Section 2: Data Ingestion and Transformation with R and Python in Power BI
Section 3: Data Enrichment with R and Python in Power BI
Section 3: Data Visualization with R in Power BI

A typical analytic scenario using large datasets

One of the most frequent activities of a data scientist is to analyze a dataset of information relevant to a business scenario. The objective of the analysis is to be able to identify associations and relationships between variables, which help in some way to discover new measurable aspects of the business (insights) and can then be used to make it grow better. It may be the case that the available data may not be sufficient to determine strong associations between variables, because any additional variables may not be considered. In this case, attempting to obtain new data that is not generated by your business but enriches the context of your dataset (a data augmentation process) can improve the strength of the statistical associations between your variables. Being able to link, for example, weather forecast data to a dataset that reports the measurements of the water level of a dam certainly introduces significant variables to better...