Business Analytics Using R - A Practical Approach

Business Analytics Using R - A Practical Approach
Author :
Publisher : Apress
Total Pages : 291
Release :
ISBN-10 : 9781484225141
ISBN-13 : 1484225147
Rating : 4/5 (41 Downloads)

Book Synopsis Business Analytics Using R - A Practical Approach by : Umesh R Hodeghatta

Download or read book Business Analytics Using R - A Practical Approach written by Umesh R Hodeghatta and published by Apress. This book was released on 2016-12-27 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. 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.


Business Analytics Using R - A Practical Approach Related Books

Business Analytics Using R - A Practical Approach
Language: en
Pages: 291
Authors: Umesh R Hodeghatta
Categories: Computers
Type: BOOK - Published: 2016-12-27 - Publisher: Apress

DOWNLOAD EBOOK

Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated
Practical Statistics for Data Scientists
Language: en
Pages: 322
Authors: Peter Bruce
Categories: Computers
Type: BOOK - Published: 2017-05-10 - Publisher: "O'Reilly Media, Inc."

DOWNLOAD EBOOK

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics r
Behavioral Data Analysis with R and Python
Language: en
Pages: 361
Authors: Florent Buisson
Categories: Business & Economics
Type: BOOK - Published: 2021-06-15 - Publisher: "O'Reilly Media, Inc."

DOWNLOAD EBOOK

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorith
Data Mining for Business Analytics
Language: en
Pages: 608
Authors: Galit Shmueli
Categories: Mathematics
Type: BOOK - Published: 2019-10-14 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Pyt
Data Mining and Business Analytics with R
Language: en
Pages: 304
Authors: Johannes Ledolter
Categories: Mathematics
Type: BOOK - Published: 2013-05-28 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. D