AI and Data Literacy is designed for independent learners or college-level courses in data analytics, statistics, and artificial intelligence. The text integrates conceptual foundations with hands-on learning through applied labs, real-world datasets, and guided exercises. Intended for full-semester academic use, this edition supports students and instructors in developing practical AI and data literacy skills.

Learning resources are available from the publisher (www.datajoyai.com).

Introduction to data and AI basics

Types of data (structured and unstructured)

Data lifecycle and data quality

Data cleaning and metadata

Basics of R programming

Data storage formats (CSV, JSON, Parquet)

Databases, cloud storage, and APIs

Introduction to machine learning

Supervised and unsupervised learning

How AI learns from data

Real-world AI applications

Basic statistics and data analysis

Data visualization and charts

Outlier detection

Prompt engineering basics

Evaluating AI outputs

Human and AI collaboration

AI ethics and fairness

Privacy and responsible AI

AI risks like bias and misinformation

Hands-on labs and practical exercises

"About this title" may belong to another edition of this title.

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