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Business Analytics Using R - A Practical Approach

  • Authors
  • Umesh R. Hodeghatta
  • Umesh Nayak

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Umesh R. Hodeghatta, Umesh Nayak
    Pages 1-15
  3. Umesh R. Hodeghatta, Umesh Nayak
    Pages 17-36
  4. Umesh R. Hodeghatta, Umesh Nayak
    Pages 37-58
  5. Umesh R. Hodeghatta, Umesh Nayak
    Pages 59-89
  6. Umesh R. Hodeghatta, Umesh Nayak
    Pages 91-130
  7. Umesh R. Hodeghatta, Umesh Nayak
    Pages 131-160
  8. Umesh R. Hodeghatta, Umesh Nayak
    Pages 161-186
  9. Umesh R. Hodeghatta, Umesh Nayak
    Pages 187-205
  10. Umesh R. Hodeghatta, Umesh Nayak
    Pages 207-231
  11. Umesh R. Hodeghatta, Umesh Nayak
    Pages 233-255
  12. Umesh R. Hodeghatta, Umesh Nayak
    Pages 257-265
  13. Back Matter
    Pages 267-280

About this book

Introduction

Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics.

This book will discuss and explore the following through examples and case studies:

  • An introduction to R: data management and R functions
  • The architecture, framework, and life cycle of a business analytics project
  • Descriptive analytics using R: descriptive statistics and data cleaning
  • Data mining: classification, association rules, and clustering 
    Predictive analytics: simple regression, multiple regression, and logistic regression 

This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book.

You will:

• Write R programs to handle data

• Build analytical models and draw useful inferences from them

• Discover the basic concepts of data mining and machine learning 

• Carry out predictive modeling

• Define a business issue as an analytical problem

Keywords

Busniess Analytics Business Analytics R Descriptive Analytics Predictive Analytics Data Mining LInear Regression Logistic Regression

Bibliographic information

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