Cole Stryker - Smarter Data Science

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Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. 
Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive.
helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments.
When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise.
By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements:
Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.

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Gandhi Sivakumar, Chief Architect and Master Inventor, IBM (Australia)

A seminal treatment for how enterprises must leverage AI. The authors provide a clear and understandable path forward for using AI across cloud, fog, and mist computing. A must read for any serious data scientist and data manager.

Raul Shneir, Director, Israel National Cyber Directorate (Israel)

As a professor at Wharton who teaches data science I often mention to my students about emerging new analytical tools such as AI that can provide valuable information to business decision makers. I also encourage them to keep abreast of such tools. Smarter Data Science will definitely make my recommended readings list. It articulates clearly how an organization can build a successful Information architecture, capitalizing on AI technologies benefits. The authors have captured many intricate themes that are relevant for my students to carry with them into the business world. Many of the ideas presented in this book will benefit those working directly in the field of data science or those that will be impacted by data science. The book also includes many critical thinking tools to ready the worker of tomorrow … and realistically, today.

Dr. Josh Eliashberg, Sebastian S. Kresge Professor of Marketing, Professor of Operations, Information, and Decisions, The Wharton School

This is an excellent guide for the data-driven organization that must build a robust information architecture to continuously deliver greater value through data science or be relegated to the past. The book will enable organizations to complete their transformative journey to sustainably leverage AI technologies that incorporate cloud-based AI tools and dueling neural networks. The guiding principles that are laid out in the book should result in the democratization of data, a data literate workforce, and a transparent AI revolution.

Taarini Gupta, Behavioral Scientist/Data Scientist, Mind Genomics Advisors

Smarter Data Science

Succeeding with Enterprise-Grade Data and AI Projects

Neal Fishman with Cole Stryker

Copyright 2020 by John Wiley Sons Inc Indianapolis Indiana Published - фото 1

Copyright © 2020 by John Wiley & Sons, Inc., Indianapolis, Indiana

Published simultaneously in Canada

ISBN: 978-1-119-69341-3

ISBN: 978-1-119-69438-0 (ebk)

ISBN: 978-1-119-69342-0 (ebk)

Manufactured in the United States of America

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About the Authors

Neal Fishmanis an IBM Distinguished Engineer and is the CTO for Data-Based Pathology within IBM's Global Business Services organization. Neal is also an Open Group Certified Distinguished IT Architect. Neal has extensive experience working with IBM's clients across six continents on complex data and AI initiatives.

Neal has previously served as a board member for several different industry communities and was the technology editor for the BRCommunity webzine. Neal has been a distance learning instructor with the University of Washington and has recorded some of his other insights in Viral Data in SOA: An Enterprise Pandemic and Enterprise Architecture Using the Zachman Framework . Neal also holds several data-related patents.

You can connect with Neal on LinkedIn at linkedin.com/in/neal-fishman-.

Cole Strykeris an author and journalist based in Los Angeles. He is the author of Epic Win for Anonymous , the story of a global gang of hackers and trolls who took on big corporations and governments, and Hacking the Future , which charts the history of anonymity and makes a case for its future as a form of cultural and political expression. His writing has appeared in Newsweek, The Nation, NBC News, Salon, Vice, Boing Boing, The NY Observer, The Huffington Post, and elsewhere.

You can connect with Cole on LinkedIn at linkedin.com/in/colestryker.

Acknowledgments

I want to express my sincere gratitude to Jim Minatel at John Wiley & Sons for giving me this opportunity. I would also like to sincerely thank my editor, Tom Dinse, for his attention to detail and for his excellent suggestions in helping to improve this book. I am very appreciative of the input provided by Tarik El-Masri, Alex Baryudin, and Elis Gitin. I would also like to thank Matt Holt, Devon Lewis, Pete Gaughan, Kenyon Brown, Kathleen Wisor, Barath Kumar Rajasekaran, Steven Stansel, Josephine Schweiloch, and Betsy Schaefer.

During my career, there have been several notable giants with whom I have worked and upon whose shoulders I clearly stand. Without these people, my career would not have taken the right turns: John Zachman, Warren Selkow, Ronald Ross, David Hay, and the late John Hall. I would like to recognize the renowned Grady Booch for his graciousness and kindness to contribute the Foreword. Finally, I would like to acknowledge the efforts of Cole Stryker for helping take this book to the next level.

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