Business Analytics Using R - A Practical Approach

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.

Table of Contents:
Table of Contents Chapter 1: Introduction (page count 10) Chapter Goal: Overview of analytics. Starts with the basics of business analytics and some use cases to build a background for the upcoming chapters. Cover some of the most widely used analytical tools and techniques. Chapter 2: Basics of R (Page count 20) Chapter Goal: This chapter introduces R tool, R environment, work space, variables, data types and fundamental tool related concepts. This chapter provides enough basics to start R programing for data analysis.   Chapter 3: R datasets and variables (page count 20) Chapter Goal:  This chapter introduces the data types, variables and data manipulations in R.  This also explores various packages of R and how they can be used for data analytics. Chapter 4: Introduction to Descriptive Analytics (page count 20) Chapter Goal: The chapter provides basic statistics required for the data analysis. The basics of statistics like population and sample, descriptive statistics like mean, median, mode and measures of dispersion etc. are discussed in this chapter Chapter 5: Business Analytics Process and Data exploration (page count 30) Chapter Goal: Data exploration, validation, and data cleaning required for the data analysis are discussed in this chapter. In this chapter, we document some of the data-cleaning techniques used in the industry. Chapter 6: Supervised Machine Learning  –  Classification (page count 30) Chapter Goal: This chapter provides an overview of machine learning and data mining techniques. In this chapter, the focus is on classification techniques. It discusses different classification techniques using R packages available to perform the classification tasks. For example: Classification using Naïve Bayes, Classification using decision trees and Building decision trees using R   Chapter 7: Unsupervised Machine Learning  – Clustering and Association Rule  (page count 20) Chapter Goal: This chapter explains unsupervised techniques to perform unsupervised machine learning data analysis such as clustering and association rule techniques.    Chapter 8: Simple Linear Regression (page count 20) Chapter Goal:  Introduces the predictive analytics techniques. Understanding simple linear regression and how to interpret the results and fit the data to linear model. Understanding the concepts such as correlation, R-Squared value, Regression Assumptions.   Chapter 9: Multiple Linear Regression (page count 30) Chapter Goal: We discuss multiple regressions in this chapter, as well as concepts like multicollinearity and adjusted R-square. Chapter 10: Logistic Regression (page count 20) Chapter Goal: Explains why logistic regression is a commonly used predictive modelling technique. In this chapter, we discuss model building using logistic regression - What is logistic regression, validating logistic regression line etc. Chapter 11: Big Data Analytics and Future Trends in Analytics Chapter Goal: This final chapter gives the basics of big data analysis ecosystem and the value of such a system in carrying out effective analysis. This chapter introduces readers to the concept of Big data analytics and  Hadoop ecosystem.