Datajoyai (45 resultados)

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

Paperback or Softback. Condición: New. Data Wrangling with SQL. Book.

Paperback or Softback. Condición: New. Programming for Analytics. Book.

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

Paperback or Softback. Condición: New. AI and Data Literacy. Book.

Paperback or Softback. Condición: New. Machine Learning and Natural Language Processing. Book.

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

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

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

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

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

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

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

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

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

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

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Taschenbuch. Condición: Neu. Neuware - 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.…

Paperback. Condición: new. Paperback. Introduction to Business Analytics is an accessible, hands-on textbook that introduces students or independent learners to the concepts, tools, and decision-making processes that drive modern organizations. Written for introductory business analytics, data analytics, and AI literacy courses, the book focuses on practical applications rather than advanced mathematics or programming.Students explore the four major types of analytics-descriptive, diagnostic, predictive, and prescriptive-and learn how organizations use data to improve operations, understand customers, forecast outcomes, and support strategic decisions. Topics include key performance indicators (KPIs), dashboards, data visualization, trend analysis, root cause analysis, forecasting, predictive modeling, classification, artificial intelligence, business communication, and data-driven decision-making.Real-world examples from companies such as Amazon, Netflix, Starbucks, and Walmart demonstrate how analytics creates business value. Hands-on labs and practical exercises help learners apply concepts using realistic business scenarios and modern analytics tools. Resources are available at Designed for community college, undergraduate, workforce training, and business students, this text provides a foundation for future study in business analytics, data science, artificial intelligence, and applied analytics. A practical text that teaches students how to use data, analytics, and AI to make better decisions. Covers descriptive, diagnostic, predictive, and prescriptive analytics with real-world examples, hands-on labs, and business applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: 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. Condición: 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 multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Machine Learning and Natural Language Processing provides an accessible, applied introduction to machine learning (ML) and natural language processing (NLP) for students, educators, and business professionals. Designed for introductory college courses in analytics, artificial intelligence, and data science, this text focuses on practical understanding rather than advanced mathematics.Readers learn how machine learning systems identify patterns in data, make predictions, support decision-making, and automate business processes. Topics include supervised and unsupervised learning, classification, regression, recommendation systems, optimization, model evaluation, performance metrics, bias, fairness, and responsible AI practices.The NLP portion of the book explores how computers process and analyze human language. Students learn text preprocessing, tokenization, stopword removal, lemmatization, TF-IDF, word embeddings, sentiment analysis, topic modeling, named entity recognition, conversational AI, text summarization, and generative AI applications.Throughout the text, real-world examples demonstrate how organizations use ML and NLP to improve customer experiences, streamline operations, analyze feedback, and support strategic decisions. Hands-on labs and guided activities help students apply concepts using modern analytics tools and datasets. Resources available at Written in a clear, student-friendly style, this book bridges the gap between theory and practice while emphasizing ethical considerations, model interpretability, and human oversight. It is ideal for introductory courses in machine learning, artificial intelligence, natural language processing, business analytics, and applied data science. Machine Learning and Natural Language Processing: Student Edition provides a hands-on introduction to machine learning and NLP for real-world applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: 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. Condición: 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. Condición: 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…