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


In this chapter, you learned a lot about the unsupervised learning technique called cluster analysis. It helps you when you do not have a response variable but you believe that there are natural groupings in the data. There are many types of clustering algorithms, and you learned two. K-means clustering is widely used and is ideal when you have constraints or a sense of how many clusters exist in your data. It is straightforward to implement and you can pull elements out of the model to perform other analysis. You used k-means to determine the best number and location of customer service kiosks. Hierarchical clustering is a good choice when you do not have a sense of the number of groups that may exist in the data. You used this to perform customer segmentation of two-dimensional demographic data. You learned how to use the elements from k-means to help evaluate the right number of clusters to select, as well as visualize the output of hierarchical clustering.

In the next chapter...