Book Image

Introduction to R for Business Intelligence

By : Jay Gendron
Book Image

Introduction to R for Business Intelligence

By: Jay Gendron

Overview of this book

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
Table of Contents (19 chapters)
Introduction to R for Business Intelligence
About the Author
About the Reviewers
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations


Congratulations! You have learned a lot of topics in this chapter. Data cleaning is a very important part of business intelligence analysis. In this chapter, you learned that cleaning data is a four-step process that can be remembered by SFCA (summarize-fix-convert-adapt).

Summarizing the data gives you a big-picture overview and provides a perspective on the data. This shapes your data cleaning strategies. Fixing flawed data can be tedious, but there are common practices to use. Converting data is important to get it in the right data type to support your analysis. Dates and times can be difficult, but tools help with this. Adapting your data to a standard is the key to setting a foundation for a successful data analysis. Standards may be given or you may design one.

Continuing to learn is also important as the packages and methods change frequently. Lastly, the topic of data cleaning is full of interesting ideas that you may find helpful. A recommended resource is An Introduction...