Data Analytics Made Accessible: 2018 edition

buy now


This book fills the need for a concise and conversational book on the growing field of Data Science. Easy to read and informative, this lucid book covers everything important, with concrete examples, and invites the reader to join this field. The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is also a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Finally, it includes a tutorial for R platform.
The 2018 edition includes a new chapter on Artificial Intelligence primer. The 2017 edition had added four new chapters in response to the thoughts and suggestions expressed by many reviewers.
The book has proved very popular throughout the world. Many universities in the US and around the world have adopted it as a textbook for their courses. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others can use this book to learn in a few hours how to make sense of and develop actionable insights from the enormous data coming their way. This is a flowing book that one can finish in one sitting, or one can return to it again and again for insights and techniques.

Table of Contents
Chapter 1: Wholeness of Data Analytics
Chapter 2: Business Intelligence Concepts & Applications
Chapter 3: Data Warehousing
Chapter 4: Data Mining
Chapter 5: Data Visualization
Chapter 6: Decision Trees
Chapter 7: Regression Models
Chapter 8: Artificial Neural Networks
Chapter 9: Cluster Analysis
Chapter 10: Association Rule Mining
Chapter 11: Text Mining
Chapter 12: Naïve Bayes Analysis
Chapter 13: Support Vector Machines
Chapter 14: Web Mining
Chapter 15: Social Network Analysis
Chapter 16: Big Data
Chapter 17: Data Modeling Primer
Chapter 18: Statistics Primer
Chapter 19: Artificial Intelligence Primer
Chapter 20: Data Science Careers
Appendix: Data Mining Tutorial using R