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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: 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 R: Data Mining Tutorial using R Appendix P: Data Mining Tutorial using Python