Data Mining Exploration by Seefeld Kimberly (8 results)

Paperback or Softback. Condition: New. Data Mining and Exploration. Book.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Paperback. Condition: new. Paperback. Data Mining and Exploration 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 book 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. Resource files can be downloaded at 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 pipelineClear explanations of clustering, classification, and association methodsPractical labs and demonstrations in every chapterIntegration of AI tools and modern analytics platformsStrong focus on ethics, bias, and responsible data useDesigned for applied, non-theoretical learners in data science programs A practical introduction to data mining and exploratory data analysis This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Data Mining and Exploration 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 book 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. Resource files can be downloaded at 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 pipelineClear explanations of clustering, classification, and association methodsPractical labs and demonstrations in every chapterIntegration of AI tools and modern analytics platformsStrong focus on ethics, bias, and responsible data useDesigned for applied, non-theoretical learners in data science programs A practical introduction to data mining and exploratory data analysis This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: new. Paperback. Data Mining and Exploration 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 book 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. Resource files can be downloaded at 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 pipelineClear explanations of clustering, classification, and association methodsPractical labs and demonstrations in every chapterIntegration of AI tools and modern analytics platformsStrong focus on ethics, bias, and responsible data useDesigned for applied, non-theoretical learners in data science programs A practical introduction to data mining and exploratory data analysis This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 pipelineClear explanations of clustering, classification, and association methodsPractical labs and demonstrations in every chapterIntegration of AI tools and modern analytics platformsStrong focus on ethics, bias, and responsible data useDesigned for applied, non-theoretical learners in data science programsThis text is ideal for introductory courses in data mining, data science, business analytics, and AI literacy.…