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paperback. Condition: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority.

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Paperback or Softback. Condition: New. Introduction to Business Analytics. Book.

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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Data Wrangling with SQL is a hands-on textbook designed to help students develop the practical skills needed to prepare real-world data for analysis. Because most data arrives incomplete, inconsistent, or poorly structured, data wrangling is often the most time-consuming and critical phase of the analytics process.Using clear explanations, business-focused examples, and guided exercises, students learn how to assess data quality, clean messy datasets, handle missing values, remove duplicates, normalize formats, merge data from multiple sources, create calculated fields, and reshape data for reporting and analysis. The text introduces essential SQL concepts in the context of solving practical data preparation challenges rather than memorizing syntax.The book also explores modern AI-assisted approaches to data cleaning, validation, and SQL query development, helping students understand how artificial intelligence can support data professionals while maintaining data quality and accuracy.Features include: - Seven chapters covering the complete data wrangling workflow- Step-by-step demonstrations and guided labs- Practice exercises and review questions- Real-world business and analytics scenarios- Coverage of joins, filtering, transformations, aggregation, and reshaping- AI-assisted data preparation techniques- Companion resources for students and instructorsIdeal for introductory data analytics, business analytics, database, data science, and information systems courses. A practical introduction to data preparation and SQL for analytics. Students learn how to clean, transform, merge, validate, and summarize real-world data through hands-on labs, business-focused examples, and AI-assisted workflows. 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 multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. 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 .Introduction to data and AI basicsTypes of data (structured and unstructured)Data lifecycle and data qualityData cleaning and metadataBasics of R programmingData storage formats (CSV, JSON, Parquet)Databases, cloud storage, and APIsIntroduction to machine learningSupervised and unsupervised learningHow AI learns from dataReal-world AI applicationsBasic statistics and data analysisData visualization and chartsOutlier detectionPrompt engineering basicsEvaluating AI outputsHuman and AI collaborationAI ethics and fairnessPrivacy and responsible AIAI risks like bias and misinformationHands-on labs and practical exercises AI and Data Literacy explores data basics, data quality, storage, machine learning, analytics, visualization, prompt engineering, AI evaluation, ethics, fairness, privacy, and responsible AI through practical labs and real-world examples. 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. Programming for Analytics introduces the fundamental programming concepts needed for modern data analytics, business intelligence, artificial intelligence, and data science. Designed for beginners with little or no coding experience, this textbook emphasizes analytical thinking, problem-solving, automation, and reproducible workflows rather than computer science theory.Students learn how programming supports data analysis, automation, reporting, and decision-making. The book introduces core concepts including variables, data types, functions, libraries, data structures, automation, debugging, and reproducibility. Readers explore both R and Python, gaining an understanding of how programming languages support analytics workflows and how skills transfer across platforms.Special attention is given to practical topics that analysts encounter in the workplace, including data manipulation, code organization, reusable functions, automation of repetitive tasks, documentation, debugging strategies, and evaluating code quality. The text also explores the growing role of artificial intelligence as a programming assistant and discusses responsible, ethical, and professional uses of AI-generated code.Hands-on labs, demonstrations, and practice exercises reinforce concepts through realistic analytics scenarios. By focusing on concepts rather than language-specific syntax, this book helps students develop a durable foundation that prepares them for future study in analytics, data science, machine learning, and AI.Ideal for introductory courses in programming for analytics, data analytics, business analytics, artificial intelligence, and data science or independent study. Resources for learning are available from the publisher. A beginner-friendly introduction to programming for analytics that teaches students how to use R, Python, automation, and AI tools to support data analysis. Includes hands-on labs, practical examples, and reproducible analytics workflows. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, 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.…