AI & data literacy : empowering citizens of data science / Bill Schmarzo.

Learn the key skills and capabilities that empower Citizens of Data Science to not only survive but thrive in an AI-dominated world. Purchase of the print or Kindle book includes a free PDF eBook Key Features Prepare for a future dominated by AI and big data Enhance your AI and data literacy with re...

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Bibliographic Details
Online Access: Full Text (via O'Reilly/Safari)
Main Author: Schmarzo, Bill (Author)
Format: eBook
Language:English
Published: Birmingham : Packt Publishing, Limited, 2023.
Subjects:
Table of Contents:
  • 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?
  • 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
  • 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
  • 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
  • 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