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Author: Maheshwari, Anil
Edition: 2024
Binding: Kindle Edition
Format: Kindle eBook
Number Of Pages: 448
Release Date: 01-05-2014
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
Languages: english