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 ContentsChapter 1: Wholeness of Data AnalyticsChapter 2: Business Intelligence Concepts & ApplicationsChapter 3: Data WarehousingChapter 4: Data MiningChapter 5: Data VisualizationChapter 6: Decision TreesChapter 7: Regression ModelsChapter 8: Artificial Neural NetworksChapter 9: Cluster Analysis Chapter 10: Association Rule Mining Chapter 11: Text MiningChapter 12: Naïve Bayes AnalysisChapter 13: Support Vector MachinesChapter 14: Web MiningChapter 15: Social Network AnalysisChapter 16: Big DataChapter 17: Data Modeling PrimerChapter 18: Statistics PrimerChapter 19: Artificial Intelligence PrimerChapter 20: Data Ownership and PrivacyChapter 21: Data Science CareersAppendix R: Data Mining Tutorial using RAppendix P: Data Mining Tutorial using Python
eBook details
Title: Data Analytics Made Accessible: 2020 edition
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