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


Well done, data adventurer. You explored a dataset pretty thoroughly. Exploratory data analysis is the preliminary analysis step as it gives you insights about the features or relationships that are best for modeling. You learned that exploratory data analysis has a structure-it is not a series of random plots. The structure begins from having general questions in mind and then becoming familiar with the data by addressing four structured questions: Look-Relationships-Correlation-Significance.

Look provides you a big-picture idea of what is in the data, what type of scales and data types are used, and verification the data is suitable. This question relies mostly on tabular output. Relationships may exist between variables, and graphical exploration is well suited to spot them. Correlations are ways to describe relationships numerically, using the sign and value of correlation coefficients. There are many ways to calculate and display them. Significance helps determine whether the...