
Business Analytics Using R - A Practical Approach
Description
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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:
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An introduction to R: data management and R functions
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The architecture, framework, and life cycle of a business analytics project
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Descriptive analytics using R: descriptive statistics and data cleaning
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Data mining: classification, association rules, and clustering
Predictiveanalytics: 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.
What You Will Learn
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
Who This Book Is For
Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.
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Content
Table of Contents
Chapter 1: Introduction
Chapter 2: Basics of R
Chapter 3: R datasets and variables
Chapter 4: Introduction to Descriptive Analytics
Chapter 5: Business Analytics Process and Data exploration
Chapter 6: Supervised Machine Learning - Classification
Chapter 7: Unsupervised Machine Learning - Clustering and Association Rule
Chapter 8: Simple Linear Regression
Chapter 9: Multiple Linear Regression
Chapter 10: Logistic Regression (page count 20)
Chapter 11: Big Data Analytics and Future Trends in Analytics
System requirements
File format: PDF
Copy protection: Watermark-DRM (Digital Rights Management)
System requirements:
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- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Watermark-DRM, a „soft” copy protection. This means that there are no technical restrictions to prevent illegal distribution. However, there is a personalised watermark embedded in the eBook that can be used to identify the purchaser of the eBook in the event of misuse and to provide evidence for legal purposes.
For more information, see our eBook Help page.