Practical Business Analytics using R

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

Umesh R. Hodeghatta, Ph.D.Dr. Umesh Rao. Hodeghatta is an acclaimed professional in the field of machine learning, NLP and business analytics. He has his master's degree in EE from Oklahoma State University, USA and Ph.D. from the Indian Institute of Technology (IIT), Kharagpur with a specialization in Machine Learning and NLP. Dr. Umesh Hodeghatta is currently working as a Data Scientist in United States serving multiple clients. He has more than 20 years of work experience and has held technical and senior management positions at XIM-Bhubaneswar, McAfee, Cisco Systems, and AT&T Bell Laboratories, USA. He has recently established IBM Big Data Analytics Lab and HP Research Lab at Xavier Univeristy. Dr. Hodeghatta has published many journal articles in international journals and conference proceedings, viz, "Understading Twitter as e-WOM", "Sentiment Analysis of Hollywood Movies on Twitter", "PCI DSS - Penalty of not being Compliant" are few of the well-known publications. In addition, he has authored a book titled "The InfoSec Handbook: An Introduction to Information Security" published by Springer Apress, USA. Dr. Hodeghatta has contributed his services to many professional organizations and regulatory bodies. He was an Executive Committee member of IEEE Computer Society (India); Academic advisory member for the Information and Security Audit Association (ISACA), USA; IT advisor for the government of Odisha, India; Technical Advisory Member of the International Neural Network Society (INNS) India; Advisory member of Task Force on Business Intelligence & Knowledge Management. Owing to these achievements, he has been listed in "World's Who's Who" of the year - 2012, 2013, 2014, 2015 - published by Marquis Who's Who, USA. He is also a senior member of the IEEE, USA. Further details about Dr. Hodeghatta is available at http://www.mytechnospeak.comUmesha Nayak is a director and principal consultant of MUSA Software Engineering Pvt. Ltd. which focuses on systems