Description
Price: ₹500.00
(as of Apr 23, 2024 00:38:12 UTC – Details)
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 and constantly updated book covers everything important, with concrete examples, and invites the reader to join this field. University of Texas calls it #1 read for Data Analysts. https://techbootcamps.utexas.edu/blog/4-books-every-data-analyst-read/
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 contained expanded primers on Big Data, Artificial Intelligence, and Data Science careers, and a full tutorial on Python. The 2020 edition contains a new chapter on Data Ownership and Privacy, as these issues have become increasingly important.
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 as a reference book for insights and techniques. Thank you!
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
Chapter 18: Statistics
Chapter 19: Artificial Intelligence
Chapter 20: Data Ownership and Privacy
Chapter 21: Data Science Careers
Appendix R: Data Mining Tutorial using R
Appendix P: Data Mining Tutorial using Python
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 contained expanded primers on Big Data, Artificial Intelligence, and Data Science careers, and a full tutorial on Python. The 2020 edition contains a new chapter on Data Ownership and Privacy, as these issues have become increasingly important.
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 as a reference book for insights and techniques. Thank you!
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
Chapter 18: Statistics
Chapter 19: Artificial Intelligence
Chapter 20: Data Ownership and Privacy
Chapter 21: Data Science Careers
Appendix R: Data Mining Tutorial using R
Appendix P: Data Mining Tutorial using Python
ASIN : B00K2I2JL8
Language : English
File size : 20380 KB
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Enabled
Print length : 382 pages
