AI and Data Literacy
Description
AI and Data Literacy: Academic Edition is a practical, hands-on guide to understanding data and artificial intelligence. It breaks down complex concepts into clear explanations and 12 guided labs that build real, usable skills.
Designed for students, educators, and lifelong learners, the book focuses on how AI and data actually work-how data is collected, cleaned, analyzed, and used to train models-so readers can think critically and use modern tools with confidence.
This edition is available as a fully disability-accessible EPUB, supporting screen readers, reflowable and resizable text, keyboard navigation, alt-text for images, and accessible semantic structure. Every learner can engage with the material effectively.
More than a textbook, it includes a complete digital learning environment with downloadable datasets, ready-to-run code, templates, walkthroughs, and sample solutions. Ideal for self-study, classrooms, and remote or hybrid instruction.
Includes 12 Hands-On LabsData vs. Information . Automation vs. AI . Survey Design & Data Lifecycle . Data Cleaning . R Programming . Data Profiling . Web APIs . Unsupervised Learning . Supervised Learning . Statistics & Visualization . Prompt Engineering . AI Ethics & Bias
Who This Book Is ForStudents, educators, beginners, professionals upskilling for an AI-driven workplace, and accessibility-focused learners and institutions.
More details
Content
1 Data, AI, and the Modern World
Learning Outcomes
Introduction
Understanding Data in the Digital Age
Artificial Intelligence Demystified
AI Applications in Daily Life and Professional Contexts
Keywords
Review Questions
Lab 1A: Exploring Data vs Information
Lab 1B: Automation vs AI
Chapter Conclusion
References
2 The Data Lifecycle and Quality
Learning Outcomes
Introduction
Understanding the Data Lifecycle
Data Quality Issues and Their Impact
Data Cleaning and Metadata Management
Keywords
Review Questions
Lab 2A: Data Lifecycle - Survey Mini Project
Lab 2B: Data Preparation
Lab 2C: R Introduction
Chapter Conclusion
References
3 Data Storage, Formats, and Access
Learning Outcomes
Introduction
Understanding Data Storage Formats
Database Fundamentals and Structured Storage
Cloud Storage and API Access
Key Terms
Review Questions
Lab 3A: Data Profiling
Lab 3B: Introduction to APIs
Chapter Conclusion
References
4 How AI Learns from Data
Learning Outcomes
Introduction
The Learning Spectrum: Supervised vs. Unsupervised
The Anatomy of Machine Learning
The Training Process: From Data to Intelligence
Real-World Applications: AI Learning in Action
Challenges and Limitations in AI Learning
The Future of AI Learning
Keywords
Review Questions
Lab 4A: Unsupervised Learning
Lab 4B: Supervised Learning
Chapter Conclusion
References
5 Introduction to Data Analysis and Visualization
Learning Outcomes
Introduction
Descriptive Statistics
Visualization: Choosing the Right Chart
Advanced Chart Selection: Matching Visualization to Purpose
Identifying Outliers: The Art of Exception Detection
Real-World Applications: Data Analysis in Action
Current Trends and Future Directions
Best Practices and Common Pitfalls
Keywords
Review Questions
Lab 5A: Descriptive Statistics
Lab 5B: Data Visualization with ggplot2
Chapter Conclusion
References
6 Using AI Tools: Prompting and Evaluation
Learning Outcomes
Introduction
Understanding Prompt Engineering
Evaluating AI Outputs
Real-World Applications: AI Tools in Action
Human-AI Collaboration
Current Trends and Future Directions
Keywords
Review Questions
Lab 6A: Understanding Prompt Engineering
Chapter Conclusion
References
7 Ethics, Fairness, and Responsible AI
Learning Outcomes
Introduction
The Foundation: Understanding AI Ethics
Bias and Fairness: The Challenge of Algorithmic Justice
Privacy, Consent, and Surveillance
AI Misuse: Deepfakes, Misinformation, and Manipulation
Ethical Frameworks for Responsible AI
Transparency, Accountability, and Accessibility
Current Trends and Future Directions
Keywords
Review Questions
Lab 7A: Detecting Bias
Lab 7B: AI-Created Data
Chapter Conclusion
References
