Data Mining and Exploration | Student Edition

Kimberly Seefeld

ISBN 10: 1969233389 / ISBN 13: 9781969233388

Published by DataJoyAI, 2026

About this Item

Data Mining and Exploration | Student Edition | Kimberly Seefeld | Taschenbuch | Englisch | 2026 | DataJoyAI | EAN 9781969233388 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 135845128

Bibliographic Details

• Title: Data Mining and Exploration | Student Edition
• Publisher: DataJoyAI
• Publication Date: 2026
• Language: English
• ISBN 10: 1969233389
• ISBN 13: 9781969233388
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 15D millimeters 254 millimeters width by 178 millimeters height by 15 millimeters depth
• Weight: 511 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.