• Complain

Daniel Vaughan - Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise

Here you can read online Daniel Vaughan - Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2020, publisher: OReilly Media, Inc, USA, genre: Home and family. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Daniel Vaughan Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise
  • Book:
    Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise
  • Author:
  • Publisher:
    OReilly Media, Inc, USA
  • Genre:
  • Year:
    2020
  • Rating:
    4 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 80
    • 1
    • 2
    • 3
    • 4
    • 5

Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

While several market-leading companies have successfully transformed through data- and AI-driven approaches to business, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the potential of this predictive revolution? This practical guide presents a battle-tested method to help you translate business decisions into tractable descriptive, predictive, and prescriptive problems. Author Daniel Vaughan shows practitioners of data science and others interested in using AI not only how to ask the right questions but also how to generate value from data and analytics using modern AI technologies and decision theory principles. Youll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues. With this book, youll learn how to: Break business decisions into stages and use predictive or prescriptive methods on each stage Identify human biases when working with uncertainty Customize optimal decisions to different customers using predictive and prescriptive methods Ask business questions with high potential for value creation through AI and data-driven methods Simplify complexity to tackle difficult business decisions with current predictive and prescriptive technologies

Daniel Vaughan: author's other books


Who wrote Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise? Find out the surname, the name of the author of the book and a list of all author's works by series.

Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
Analytical Skills for AI and Data Science

by Daniel Vaughan

Copyright 2020 Daniel Vaughan. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

  • Acquisitions Editor: Jonathan Hassell
  • Development Editor: Michele Cronin
  • Production Editor: Daniel Elfanbaum
  • Copyeditor: Piper Editorial
  • Proofreader: Justin Billing
  • Indexer: Sue Klefstad
  • Interior Designer: David Futato
  • Cover Designer: Karen Montgomery
  • Illustrator: Rebecca Demarest
  • May 2020: First Edition
Revision History for the First Edition
  • 2020-05-21: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781492060949 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Analytical Skills for AI and Data Science, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

The views expressed in this work are those of the author, and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-492-06094-9

[LSI]

Preface
Why Analytical Skills for AI?

Judging from the headlines and commentary in social media during the second half of the 2010s, the age of artificial intelligence has finally arrived with its promises of automation and value creation. Not too long ago, a similar promise came with the big data revolution that started around 2005. And while it is true that some select companies have been able to disrupt industries through AI- and data-driven business models, many have yet to realize the promises.

There are several explanations for this lack of measurable resultsall with some validity, surelybut the one put forward in this book is the general lack of analytical skills that are complementary to these new technologies.

The central premise of the book is that value at the enterprise is created by making decisions, not with data or predictive technologies alone. Nonetheless, we can piggyback on the big data and AI revolutions and start making better choices in a systematic and scalable way, by transforming our companies into modern AI- and data-driven decision-making enterprises.

To make better decisions, we first need to ask the right questions, forcing us to move from descriptive and predictive analyses to prescriptive courses of action. I will devote the first few chapters to clarifying these concepts and explaining how to ask better business questions suitable for this type of analysis. I will then delve into the anatomy of decision-making, starting with the consequences or outcomes we want to achieve, moving backward to the actions we can take, and discussing the problems and opportunities created by intervening uncertainty and causality. Finally, we will learn how to pose and solve prescriptive problems.

Use Case-Driven Approach

Since my aim is to help practitioners to create value from AI and data science using this analytical skillset, each chapter will illustrate how each skill works with the help of a collection of use cases. These were selected because Ive found them valuable at work, because of their generality across industries, because students found them particularly interesting or useful, or because they are important building blocks for more complex problems commonly found in the industry. But in the end this choice was subjective, and depending on your industry, they may be more or less relevant.

What This Book Isnt

This book isnt about artificial intelligence or machine learning. This book is about the extra skills needed to be successful at creating value from these predictive technologies.

I have provided an introduction to machine learning in the Appendix for the purpose of being self-contained, but it isnt a detailed presentation of machine learning-related material nor was it planned as one. For that, you can consult many of the great books out there (some are mentioned in the Further Reading section of the Appendix).

Who This Book Is For

This book is for anyone wanting to create value from machine learning. Ive used parts of the material with business students, data scientists, and business people alike.

The most advanced material deals with decision-making under uncertainty and optimization, so having a background in probability, statistics, or calculus will definitely help. For readers without this background, Ive tried to make the presentation self-contained. On a first pass, you might just skip the technical details and focus on developing an intuition and an understanding of the main messages for each chapter.

  • If youre a business person with no interest whatsoever in doing machine learning yourself, this book should at least help redirect the questions you want your data scientists to answer. Business people have great ideas, but they may have difficulty expressing what they want to more technical types. If you want to start using AI in your own line of work, this book will help you formulate and translate the questions so that others can work on the solutions. My hope is that it will also serve as inspiration to solve new problems you didnt think were resolvable.

  • If youre a data scientist, this book will provide a holistic view of how you can approach your stakeholders and generate ideas to apply your technical knowledge. In my experience, data scientists become really good at solving predictive problems, but many times have difficulties delivering prescriptive courses of action. The result is that your work doesnt create as much value as you want and expect. If youve felt frustrated because your stakeholders dont understand the relevance of machine learning, this book could help you transform the question youre solving to take it closer to the business.

  • If youre neither one of these, the fact that you find the title compelling indicates that you have an interest in AI. Please recall the disclaimer in the previous section: you wont learn to develop AI solutions in this book. My aim is to help you translate business questions into prescriptive solutions using AI as an input.

Whats Needed

I wrote this book in a style that is supposed to be readable for very different audiences. I do not expect the reader to have any prior knowledge of probability or statistics, machine learning, economics, or the theory of decision making.

Readers with such backgrounds will find the more technical material introductory, and thats actually great. In my opinion, the key to creating value through these techniques is to focus on the business question and not on the technical details. I hope that by focusing on the use cases you can find many new ways to solve the problems youre facing.

Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise»

Look at similar books to Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise»

Discussion, reviews of the book Analytical Skills for AI and Data Science: Building Skills for an Ai-Driven Enterprise and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.