Data Mining and Exploration - Student Edition provides a comprehensive, hands-on introduction to the core methods used in modern data analytics and machine learning. Designed for community college and undergraduate students, this textbook bridges foundational statistical thinking with applied data mining techniques used in today's AI-driven world.
The book begins with the history and evolution of data mining and progresses through essential topics including descriptive statistics, data acquisition, data cleaning, transformation, clustering, classification, and association analysis. Each chapter integrates practical tools such as R, Python environments, and AI-assisted analytics platforms, making abstract concepts accessible through real-world applications.
Special emphasis is placed on modern developments in the field, including AI-enhanced data mining, automated feature engineering, natural language processing, and ethical AI practices. Students are guided through structured labs that reinforce learning through hands-on practice and applied problem-solving.
Key Features:
Full coverage of the data mining pipeline
Clear explanations of clustering, classification, and association methods
Practical labs and demonstrations in every chapter
Integration of AI tools and modern analytics platforms
Strong focus on ethics, bias, and responsible data use
Designed for applied, non-theoretical learners in data science programs
This text is ideal for introductory courses in data mining, data science, business analytics, and AI literacy.
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