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

Understanding exploratory data analysis


"An approximate answer to the right question is worth a great deal more than a precise answer to the wrong question."

 --John Tukey

Exploratory data analysis is a fascinating area as it blends the art of conversation, skills of data science, and aspects of the domain being studied. It is a structured process where you discover information about the data characteristics and relationships among two or more variables.

Questions matter

The biostatistician, Roger Peng, has said that developing questions is a practical way of reducing the exponential number of ways you can explore a dataset. In particular, a sharp question or hypothesis can serve as a dimension reduction tool that can eliminate variables that are not immediately relevant to the question (Peng, 2015, p. 17).

The use case highlights the importance of asking good questions. Your success in conducting exploratory data analysis will rely on your skills as an investigator. For a moment, take on...