Dhaval Maheta

This book is a comprehensive, step-by-step learning guide towards understanding an entire value chain of Business Analytics, its interrelated components and its role in business decision-making in India and globally. The book has been written with an interdisciplinary approach that triggers strategic as well as routine, thought-provoking ideas to cut across data from several business domains globally. Business Analytics Value Chain deals with the end-to-end journey from planning the approach to a data enriched decision-problem, to communicating results derived from analytics models to clients. Using current cases from all aspects of a business venture (finance, marketing, human resources, and operations), the book helps the readers to develop the capabilities of evaluating a business case scenario; understand the business problem; identify the data sources and data availability; logically think through problemsolving; use analytics techniques and application software to solve the problem; and be able to interpret the results. Case studies have been carefully designed to represent business scenarios from varied business domains, both local and global, such that they guide the students to making informed fact-based decisions during collaborative planning, analyzing, interpreting, and communicating outcomes for data-enriched problem scenarios. The book will be useful for students, researchers, and instructors from the fields of Business Management, Data Analytics, Commerce, and Economics. It will also be an indispensable companion to the professional working in the field of data analytics.

This book is a comprehensive, step-by-step learning guide towards understanding an entire value chain of Business Analytics, its interrelated components and its role in business decision-making in India and globally. The book has been written with an interdisciplinary approach that triggers strategic as well as routine, thought-provoking ideas to cut across data from several business domains globally. Business Analytics Value Chain deals with the end-to-end journey from planning the approach to a data enriched decision-problem, to communicating results derived from analytics models to clients. Using current cases from all aspects of a business venture (finance, marketing, human resources, and operations), the book helps the readers to develop the capabilities of evaluating a business case scenario; understand the business problem; identify the data sources and data availability; logically think through problemsolving; use analytics techniques and application software to solve the problem; and be able to interpret the results. Case studies have been carefully designed to represent business scenarios from varied business domains, both local and global, such that they guide the students to making informed fact-based decisions during collaborative planning, analyzing, interpreting, and communicating outcomes for data-enriched problem scenarios. The book will be useful for students, researchers, and instructors from the fields of Business Management, Data Analytics, Commerce, and Economics. It will also be an indispensable companion to the professional working in the field of data analytics.

The objective of this book is to address the needs of students in using R-Rattle software for data mining and data modelling techniques. The pacing and content of this book is set in such a way so that it gives user friendly approach to students and other learners. R is a statistical computing environment. It is free (open source) software for statistical computation and graphics and a computer language designed for typical statistical and graphical applications. R is gaining a wider acceptance because it includes the ability to save and run commands stored in script files, and an integrated editor in the R Graphical User Interface (R-GUI). It is available for most platforms including unix/linux, PC, and Macintosh platforms. Rattle package is hosted on bitbucket where anyone can contribute to the package. It deploys more than 100 packages to build robust models in data science. It is the only GUI in R for machine learning and data mining. It presents statistical and visual summaries of data, transforms the data, builds the supervised and unsupervised model.