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

Chapter 5. Forecasting with R

R is a popular programming language for statisticians and data scientists. This is primarily due to its popularity with students and professors in the academic world. R is a free and open source language that can be taught in any statistics class with minimal difficulty.

Within the last couple of years, R has creeped into the business intelligence landscape due to the integration of R with enterprise and desktop visualization tools such as Microsoft Power BI and Tableau. During this same period, many academics transitioned from research into the corporate world, and with them came their knowledge of R. While R is known for its predictive capabilities, many are surprised to find that it is a great visualization tool with many libraries, made available by its vast community, such as ggplot2.

In addition to the influx of R into the workforce, the introduction of RStudio into the market in 2011 brought added exposure to R. As we noted earlier in Chapter 2, Web Scraping...