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Introduction to R for Business Intelligence

You're reading from  Introduction to R for Business Intelligence

Product type Book
Published in Aug 2016
Publisher Packt
ISBN-13 9781785280252
Pages 228 pages
Edition 1st Edition
Languages
Author (1):
Jay Gendron Jay Gendron
Profile icon Jay Gendron
Toc

Table of Contents (19) Chapters close

Introduction to R for Business Intelligence
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
1. Extract, Transform, and Load 2. Data Cleaning 3. Exploratory Data Analysis 4. Linear Regression for Business 5. Data Mining with Cluster Analysis 6. Time Series Analysis 7. Visualizing the Datas Story 8. Web Dashboards with Shiny References
Other Helpful R Functions 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...

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