AI & data literacy empowering citizens of data science

Additionally, we'll discuss how organizations of all sizes can leverage AI and data to engineer or create value. We'll establish why economies of learning are more powerful than the economies of scale in a digital-centric world. Ethics and personal/organizational empowerment in the context...

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Bibliographic Details
Main Author: Schmarzo, Bill
Format: eBook
Language:English
Published: Birmingham Packt Publishing, Limited 2023
Subjects:
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • How to optimize AI-based learning systems
  • Understand user intent
  • Build diversity
  • Summary
  • Chapter 05: Making Informed Decisions
  • Factors influencing human decisions
  • Human decision-making traps
  • Trap #1: Over-confidence bias
  • Trap #2: Anchoring bias
  • Trap #3: Risk aversion
  • Trap #4: Sunk costs
  • Trap #5: Framing
  • Trap #6: Bandwagon effect
  • Trap #7: Confirmation bias
  • Trap #8: Decisions based on averages
  • Avoiding decision-making traps
  • Exploring decision-making strategies
  • Informed decision-making framework
  • Decision matrix
  • Pugh decision matrix
  • How is data collected/captured?
  • Sensors, surveillance, and IoT
  • Third-party data aggregators
  • Understanding data privacy efforts and their efficacy
  • Data privacy ramifications
  • Data privacy statements
  • How organizations monetize your personal data
  • Summary
  • References
  • Chapter 03: Analytics Literacy
  • BI vs. data science
  • What is BI?
  • What is data science?
  • The differences between BI and data science
  • Understanding the data science development process
  • The critical role of design thinking
  • Navigating the analytics maturity index
  • Level 1: Operational reporting
  • Cover
  • Copyright
  • Endorsements
  • Contributor
  • Table of Contents
  • Preface
  • Chapter 01: Why AI and Data Literacy?
  • History of literacy
  • Understanding AI
  • Dangers and risks of AI
  • AI Bill of Rights
  • Data + AI: Weapons of math destruction
  • Importance of AI and data literacy
  • What is ethics?
  • Addressing AI and data literacy challenges
  • The AI and Data Literacy Framework
  • Assessing your AI and data literacy
  • Summary
  • References
  • Chapter 02: Data and Privacy Awareness
  • Understanding data
  • What is big data?
  • What is synthetic data?
  • OODA loop
  • Critical thinking in decision making
  • Summary
  • References
  • Chapter 06: Prediction and Statistics
  • What is prediction?
  • Understanding probabilities and statistics
  • Probabilities are still just probabilities, not facts
  • Introducing the confusion matrix
  • False positives, false negatives, and AI model confirmation bias
  • Real-world use case: AI in the world of job applicants
  • Summary
  • References
  • Chapter 07: Value Engineering Competency
  • What is economics? What is value?
  • What is nanoeconomics?
  • Data and AI Analytics Business Model Maturity Index
  • Stages
  • Level 2: Insights and foresight
  • Statistical analytics
  • Exploratory analytics
  • Diagnostic analytics
  • Machine learning
  • Level 3: Augmented human intelligence
  • Neural networks
  • Regression analysis
  • Recommendation engines
  • Federated learning
  • Level 4: Autonomous analytics
  • Reinforcement learning
  • Generative AI
  • Artificial General Intelligence
  • Summary
  • Chapter 04: Understanding How AI Works
  • How does AI work?
  • What constitutes a healthy AI utility function?
  • Defining "value"
  • Understanding leading vs. lagging indicators