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 truly deserve recognition for getting through a very tough topic. You now have more awareness about time series analysis than some people with formal statistical training. You learned first-hand why linear regression is inappropriate for data that violates the assumption of independence. For time series (dependent) data, there is an assumption that the data is stationary. When the data does not meet this assumption, you can apply techniques such as differencing to help make it stationary. The R software includes specialized tools to create, inspect, and even decompose time-based data. This provided you with clues as you learned about the ARIMA model, which is a combination of three different modeling techniques applied simultaneously. These techniques, similar to other models, require both art and science in their use. Additionally, you can apply the ARIMA model to non-seasonal and seasonal components, when both exist. Finally, you learned that you must have...