This constantly evolving and updated book continues to fill 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.This edition, revised last in April 2023, includes Data Lakes, Recommendation Engines, ChatGPT, types of AI system such as Transformer systems, and a Sample Data Mining Project report. it also has a brand new chapter on Data Wrangling, which takes up 80-90% of a Data Science Project.
Many top public US universities (e.g. Texas, North Carolina, Minnesota) call it #1 read for Data Analysts.
https://techbootcamps.utexas.edu/blog/4-books-every-data-analyst-read/
https://bootcamp.unc.edu/blog/7-data-analytics-books-you-should-read-in-2019/
https://bootcamp.umn.edu/blog/7-data-analytics-books-you-should-read-in-2019/
University of California at Berkeley lists it in top 10.
https://bootcamp.berkeley.edu/blog/17-data-analytics-books-you-should-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 tool-set of the major data mining techniques and platforms. The 2025 edition has a summary chapter that encapsulates the entire book in just main 50 points in a few pages. Finally, it includes a tutorial for R and a tutorial for Python. It contains expanded primers on Big Data, Artificial Intelligence, Data Science careers, and Data Ownership and Privacy. The 2025 edition is updated with relationship to Artificial Intelligence in many ways. It includes topics such as Data Lakes, and Data sharing practices. This constantly evolving 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 22: Main Points of Data Analytics 23. Data Wrangling. Appendix R: Data Mining Tutorial using R Appendix P: Data Mining Tutorial using Python Appendix T Sample Data Mining Project

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