- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690)
Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology?
This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.
Contents
Foreword Number One. Foreword Number Two. Foreword Number Three. Preface. Endorsements. Authors. Chapter 1. You Need This Book. Chapter 2. Building a Successful Program. Chapter 3. Some Fundamentals - Process, Data, and Models. Chapter 4. It's All Analytics! Chapter 5. What Are Business Intelligence (BI) and Visual BI? Chapter 6. What Are Machine Learning and Data Mining? Chapter 7. AI (Artificial Intelligence) and How It Differs from Machine Learning. Chapter 8. What Is Data Science? Chapter 9. Big Data and Bigger Data, Little Data, Cloud, and Other Data. Chapter 10. Statistics, Causation, and Prescriptive Analytics. Chapter 11. Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More). Chapter 12. Looking Ahead. Index.
