Data Analytics Made Accessible: 2022 edition by Anil Maheshwari
Requirements: .ePUB reader, 2.2 MB
Overview: 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. 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 and a tutorial for Python. It contains expanded primers on Big Data, Artificial Intelligence, Data Science careers, and Data Ownership and Privacy. The 2022 edition is updated with relationship to Artificial Intelligence in many ways. It also includes topics such as Data Lakes. 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.
Genre: Non-Fiction >Tech & Devices
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
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