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Analytical Thinking for AI and Data Science

Author Daniel Vaughan
Release Date: 2020/09/01
ISBN: 9781492060932
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Book Description

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. You’ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.

With this book, you’ll 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

Table of Contents

  1. Preface
    1. Why analytical skills for AI
    2. Use-case-driven approach
    3. What this book isn’t
    4. Who is this book for
    5. What’s needed
    6. Conventions Used in This Book
    7. Using Code Examples
    8. O’Reilly Online Learning
    9. How to Contact Us
    10. Acknowledgments
  2. 1. Analytical thinking and the AI-driven enterprise
    1. AI is all about making predictions
      1. How does prediction work
      2. Why is everyone talking about AI today: Deep Learning
    2. How is AI transforming our businesses
      1. Better predictions may produce better decisions
      2. Data translators: identifying business cases for prediction
      3. Job automation and demand for complementary skills
    3. The Data Revolution: or is it?
      1. The 3 V’s
      2. Data-driven culture
      3. From pillars to recipes for success
      4. A tale of unrealized promises
    4. Analytical reasoning for the modern AI-driven enterprise
    5. Key takeways
    6. Futher Reading
  3. 2. Intro to Analytical Thinking
    1. Descriptive, Predictive and Prescriptive Questions
      1. When is predictive analysis powerful: the case of cancer detection
      2. Descriptive Analysis: the case of customer churn
    2. Business questions and KPIs
      1. KPIs to measure the success of a loyalty program
    3. An Anatomy of a Decision: a simple decomposition
      1. An example: why did you buy this book?
    4. A primer on causation
      1. Defining correlation and causation
      2. Understanding Causality: some examples
      3. Some difficulties in estimating causal effects
      4. A primer on A/B testing
    5. Uncertainty
      1. Uncertainty from simplification
      2. Uncertainty from heterogeneity
      3. Uncertainty from interactions
      4. Uncertainty from ignorance
    6. Key takeaways
    7. Further Reading