Burk Scott - Its All Analytics!

Its All Analytics!

The Foundations of AI, Big Data, and Data
Science Landscape for Professionals in
Healthcare, Business, and Government

Its All Analytics!

The Foundations of AI, Big Data, and Data
Science Landscape for Professionals in
Healthcare, Business, and Government

Scott Burk, Ph.D.
Gary D. Miner, Ph.D.

First edition published 2020
by CRC Press
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ISBN: 978-0-367-35968-3 (hbk)
ISBN: 978-0-367-49379-0 (pbk)
ISBN: 978-0-429-34398-8 (ebk)

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Contents

The applications of computational methods in machine learning and artificial intelligence are rapidly changing the world that we work and live in. Many traditional industries and professions are being fundamentally reimagined as AI industries. It is becoming imperative for those at every level in companies and organizations (not to mention the general public) to understand both what will AI do FOR me? and what will AI do TO me?.

The rapid acceleration in the development and deployment of these technologies is creating an increasing gap in understanding. Many who need to know dont even know what they dont know. This, coupled with hyperbolic news releases on some new AI application-of-the-moment, leaves the nontechnical observer with no easy solution to bridging this gap.

Fortunately, Scott Burk and Gary Miner have astutely recognized this gap in understanding and offer a starting point for bridging this gap in It&$3pos;s All Analytics! This volume provides a 20,000 foot overview of these technologies and serves as an easily-grasped read for beginning the journey to deeper understanding or broadening ones knowledge base. While it is geared towards those with little or no understanding of AI and machine learning, it is a valuable resource for those working in these areas who may have a siloed view of the fields.

The authors are uniquely qualified to deliver this overview as they are both not only industry practitioners of these technologies, but also educators skilled at making these topics accessible to the neophyte. They have obviously paid great attention to readability and organized the material in a way that provides a memorable framework for pinning the reader&$3pos;s newly gathered knowledge. Additionally, the book is richly referenced with additional resources for taking a deeper dive into specific subject matter.

I&$3pos;d like to be among the first to congratulate the authors on this timely, engaging, useful, and highly informative read.

John W. Cromwell, M.D., FACS, FASCRS
Associate Chief Medical Officer | Director of Surgical Quality and Safety University of Iowa Hospitals & Clinics
Director, Division of Gastrointestinal, Minimally Invasive, and Bariatric Surgery Clinical Associate Professor
University of Iowa Carver College of Medicine Faculty, Interdisciplinary Graduate Program in Informatics
University of Iowa Graduate College Iowa City, Iowa

This book seeks to reduce the sea of terms in Data Science to a systematic terminology to describe general aspects of AI and Data Science. This system of terms will permit multiple stakeholders in an organization to speak the same language across the enterprise. This common language will permit close integration between analytics and those functions in the organization that precede analytics (e.g. database design and management) and those deployment functions that follow it (e.g. marketing campaigns).

This book is not a complete expression of the subject, yet it is comprehensive in terms of the scope of each part without being comprehensive in detail. This book is not designed to show the learner how to build an analytical model with a given tool. Rather, it shows learners how to organize their thinking about the plethora of terms and concepts in Data Science to provide sufficient insight to permit practitioners to do it properly later on. Many books are available to show precise sequences of steps with a given tool for building analytical models

This book is Part I of a three-part series, each one of which will be covered in a separate book. Part II will describe: (1) the design and management of the database functions necessary to support analytics properly; (2) The structure of the analytics process (e.g. the CRISP-DM analytics process model), and; (3) The general structure of a modeling application (e.g. the major steps in building a predictive analytics model). Part III will present a survey of analytics applications in business and industry. Neither of these books should be considered as a stand-alone resource in the overall process of applying analytics in an organization. All 3 parts should be combined to compose the design of any analytics application in an organization. Development of models without the proper deployment system may relegate the models to the shelf, because they cant be deployed efficiently in the organization.

Each book (and indeed each chapter) is written to be independent of information in previous chapters, as far as possible. This degree of independence is facilitated by repeat of relevant terms and principles introduced in previous chapters, which are required to understand the information in the current chapter. As such, these chapters are learning objects, which are more or less self-contained. These learning objects must be combined together to form a seamless solution during execution.

Both this book and the entire series present a layered approach to learning the practice of Data Science. Each layer is designed to be as functionally independent as possible, yet easily related to previous layers and subsequent layers. This layered learning approach follows the Layered Learning Practice Model (LLPM) shown to be very effective in training learners to provide specific clinical or patient services in the practice of Oncology.