Book Image

Practical Business Intelligence

By : Ahmed Sherif
Book Image

Practical Business Intelligence

By: Ahmed Sherif

Overview of this book

Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis. Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business. The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company. It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market.
Table of Contents (16 chapters)
Practical Business Intelligence
About the Author
About the Reviewer
Customer Feedback


We have covered quite a bit in this chapter to get us started with both R and Python but our work will pay off as we move along with subsequent chapters. We went through two exercises to scrape data from GitHub using both R and Python. As can be seen, both tools have popular packages that allow for easy scraping of data. Both approaches were described in detail to allow you to find which process works better for you. Python is more generally known as a web scraping software tool; however, R has similar capabilities for similar tasks. Both approaches were presented to offer you more tools to keep in your toolbox. These are not the only packages that either programming language has to offer to allow for web scraping, but they are some of the more popular ones. Further investigation will show many other scraping packages such as scrapy for Python.

In the next chapter, we will begin our BI development with Microsoft Excel and PowerBI.