Data Analytics Made Accessible: 2024 edition

Data Analytics Made Accessible: 2024 edition

This book fills the need for a concise and conversational book on the hot and 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.
The 2019 edition contains expanded primers on Big Data, Artificial Intelligence, and Data Science careers. For the first time, it now includes a full tutorial on Python.
The book has proved very popular throughout the world. Dozens of universities 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: NaÃ¯ve 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 R: Data Mining Tutorial using R
Appendix P: Data Mining Tutorial using Python
Country USA
Binding Kindle Edition
ReleaseDate 2014-05-01
Format Kindle eBook

Country	USA
Binding	Kindle Edition
ReleaseDate	2014-05-01
Format	Kindle eBook