Data Science (18 Ergebnisse)

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Paperback. Zustand: new. Paperback. In the age of data, where vast volumes of information are generated daily, making sense of data has become both an art and a science. Data visualization stands at the crossroads of statistics, design, and storytelling. It plays a pivotal role in how we communicate insights, make decisions, and understand the patterns hidden within raw data."Data Visualization with Excel and Tableau: A Practical Guide for Data Science Students" is crafted to serve as a comprehensive, hands-on textbook tailored for undergraduate and postgraduate students pursuing Data Science, Analytics, Computer Applications, and Management courses. The book's main goal is to provide a deep understanding of the concepts, techniques, and tools necessary to visualize data effectively using two of the most widely-used platforms: Microsoft Excel and Tableau.Excel, being ubiquitous and easily accessible, is a perfect starting point for beginners. It introduces fundamental concepts such as charts, graphs, dashboards, and conditional formatting. On the other hand, Tableau is a professional-grade data visualization and Business Intelligence (BI) tool that offers robust capabilities for dynamic, interactive visual storytelling. When combined, these tools provide learners with a powerful arsenal to explore, analyze, and present data.This book emphasizes practical learning. Each chapter includes use cases, exercises, and real-world datasets to ensure that students not only understand theoretical concepts but also gain the skills necessary to apply them in business, research, and analytics contexts. Key FeaturesComprehensive Coverage: From the basics of data visualization to advanced dashboard design and storytelling.Hands-on Approach: Real datasets, industry case studies, and practical assignments.Tool Comparison: In-depth comparison of Excel and Tableau, guiding learners on when and how to use each.Step-by-Step Tutorials: Visual walkthroughs for building charts, dashboards, and data stories.Focus on Data Science Applications: Examples in sales, healthcare, finance, HR, and supply chain.Integration Guidance: Shows how to connect Excel and Tableau with each other and with other data platforms.Interview and Career Prep: Glossary, cheat sheets, and top interview questions on visualization tools. Who Should Read This Book?Students of Data Science, Business Analytics, BCA, MCA, BBA, MBA, BSc (CS/IT).Educators looking for structured content to teach data visualization.Entry-level data analysts, business analysts, and visualization enthusiasts.Professionals transitioning into BI or analytics roles. Chapter SummariesChapter 1: Foundations of Data VisualizationThis chapter lays the groundwork by defining what data visualization is and explaining its significance in the data science lifecycle. It elaborates on the cognitive benefits of visualizing data and introduces the key principles of design thinking, storytelling, and perception in data communication.Topics include: Definition and evolution of data visualizationImportance in data-driven decision makingTypes of data (nominal, ordinal, interval, ratio)Choosing the right chartEthical considerations and avoiding misleading visualsChapter 2: Tools Overview - Excel vs. TableauProvides a comparative introduction to Excel and Tableau. It covers user interfaces, capabilities, data connection options, and learning curves. It also includes a decision matrix to help learners choose the right tool based on the problem context.Chapter 3-6: Data Visualization Using ExcelThese chapters dive into Excel-bas Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Zustand: new. Paperback. In the age of data, where vast volumes of information are generated daily, making sense of data has become both an art and a science. Data visualization stands at the crossroads of statistics, design, and storytelling. It plays a pivotal role in how we communicate insights, make decisions, and understand the patterns hidden within raw data."Data Visualization with Excel and Tableau: A Practical Guide for Data Science Students" is crafted to serve as a comprehensive, hands-on textbook tailored for undergraduate and postgraduate students pursuing Data Science, Analytics, Computer Applications, and Management courses. The book's main goal is to provide a deep understanding of the concepts, techniques, and tools necessary to visualize data effectively using two of the most widely-used platforms: Microsoft Excel and Tableau.Excel, being ubiquitous and easily accessible, is a perfect starting point for beginners. It introduces fundamental concepts such as charts, graphs, dashboards, and conditional formatting. On the other hand, Tableau is a professional-grade data visualization and Business Intelligence (BI) tool that offers robust capabilities for dynamic, interactive visual storytelling. When combined, these tools provide learners with a powerful arsenal to explore, analyze, and present data.This book emphasizes practical learning. Each chapter includes use cases, exercises, and real-world datasets to ensure that students not only understand theoretical concepts but also gain the skills necessary to apply them in business, research, and analytics contexts. Key FeaturesComprehensive Coverage: From the basics of data visualization to advanced dashboard design and storytelling.Hands-on Approach: Real datasets, industry case studies, and practical assignments.Tool Comparison: In-depth comparison of Excel and Tableau, guiding learners on when and how to use each.Step-by-Step Tutorials: Visual walkthroughs for building charts, dashboards, and data stories.Focus on Data Science Applications: Examples in sales, healthcare, finance, HR, and supply chain.Integration Guidance: Shows how to connect Excel and Tableau with each other and with other data platforms.Interview and Career Prep: Glossary, cheat sheets, and top interview questions on visualization tools. Who Should Read This Book?Students of Data Science, Business Analytics, BCA, MCA, BBA, MBA, BSc (CS/IT).Educators looking for structured content to teach data visualization.Entry-level data analysts, business analysts, and visualization enthusiasts.Professionals transitioning into BI or analytics roles. Chapter SummariesChapter 1: Foundations of Data VisualizationThis chapter lays the groundwork by defining what data visualization is and explaining its significance in the data science lifecycle. It elaborates on the cognitive benefits of visualizing data and introduces the key principles of design thinking, storytelling, and perception in data communication.Topics include: Definition and evolution of data visualizationImportance in data-driven decision makingTypes of data (nominal, ordinal, interval, ratio)Choosing the right chartEthical considerations and avoiding misleading visualsChapter 2: Tools Overview - Excel vs. TableauProvides a comparative introduction to Excel and Tableau. It covers user interfaces, capabilities, data connection options, and learning curves. It also includes a decision matrix to help learners choose the right tool based on the problem context.Chapter 3-6: Data Visualization Using ExcelThese chapters dive i Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. "Web Scripting and API Integration for Data Science: A Practical Guide to Building Data-Driven Applications" is a comprehensive and practical book designed specifically for data science students and professionals who want to master the essential web scripting and API skills needed to interact with, extract, and manipulate data from web sources and services.In today's data-driven world, data scientists are not only expected to analyze and interpret vast datasets but also to collect, integrate, and manage data from diverse sources, many of which reside on the web and are accessible through APIs (Application Programming Interfaces). This book bridges the gap between core data science knowledge and web technologies by empowering readers with the hands-on skills needed to build real-world data applications using web scripting and APIs. What This Book CoversThis book is structured to guide readers step-by-step, starting from the fundamental concepts of web scripting languages like JavaScript and Python, progressing through the intricacies of consuming and creating RESTful APIs, and culminating in advanced topics such as secure API authentication, automation of data pipelines, and integration with machine learning workflows.It begins by demystifying web scripting - explaining how client-side and server-side scripting work, why they are crucial for data collection, and how data scientists can leverage them to streamline workflows.The core of the book focuses on APIs, which have become the backbone of modern data exchange. Readers will learn what APIs are, the different types (REST, SOAP, GraphQL), and practical methods to consume data using JavaScript and Python.Advanced chapters explore how to build your own REST API services, authenticate API calls securely, and handle real-time data streams.The book also emphasizes visualization and automation - teaching how to use web data effectively in dashboards and automate data collection for continuous, real-time insights.Finally, it presents two comprehensive project-based chapters, enabling readers to apply their knowledge to build functional dashboards and deploy APIs integrated with machine learning models. Who Should Read This Book?This book is ideal for: Data Science Students: Whether at undergraduate, postgraduate, or professional certification levels, students who want to expand their data science toolkit with practical web and API skills will find this book invaluable. It complements traditional data science curricula by adding a web-focused perspective critical for handling live and streaming data sources.Aspiring Data Scientists and Analysts: Professionals aiming to work in industries where data is sourced from multiple APIs, such as finance, healthcare, marketing, and social media analytics, will benefit by learning how to programmatically access, manipulate, and visualize data.Software Developers Transitioning to Data Science: Programmers with some coding background who want to upskill in data-centric applications will find the web scripting and API integration sections accessible and directly applicable.Researchers and Academics: Those who need to gather large datasets from web services or automate data retrieval for experiments and analysis will gain effective methods for scaling their data collection.Data Engineers and Machine Learning Practitioners: Professionals focused on building data pipelines and deploying machine learning models that consume live data from APIs will find practical guidance in creating APIs and automating workflows.Technical Enthusiasts and Hobbyists: Anyone interested in the intersection of web technologies and data science, looking t Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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PAP. Zustand: 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. Zustand: 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. Zustand: 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. Zustand: new. Paperback. About the BookIn today's data driven and algorithm powered world, the need to understand numerical computation and statistical analysis has never been more crucial. Numerical Methods and Statistical Computing A Practical Approach for BCA, MCA, and BTech Students is a comprehensive and student friendly textbook designed to meet the academic and applied requirements of undergraduate and postgraduate computer science and engineering programs.This book covers both classical numerical techniques and modern statistical methods in a unified manner, providing a balanced mix of theoretical background, real world applications, and hands on practice. Whether you are a BCA student trying to master numerical roots, an MCA scholar exploring curve fitting, or a BTech student solving differential equations or analyzing statistical data, this book serves as a complete guide.Organized into 13 well structured chapters, the content aligns with university curricula and offers a progressive learning path from foundational concepts in statistics and probability to advanced numerical methods used in computation, analysis, engineering simulations, and scientific research.What This Book CoversFoundations of StatisticsLearn how to represent and summarize data using measures of central tendency, dispersion, and graphical techniques such as histograms and scatter plots.Correlation and Regression AnalysisUnderstand relationships between variables using Pearson and Spearman correlation, and apply regression analysis for prediction and modeling.Probability and Random VariablesGain insight into random phenomena using discrete and continuous probability distributions, and calculate expected values and variances.Error AnalysisDiscover how errors affect digital computations, and explore the concepts of round off error, truncation error, convergence, and algorithm efficiency.Numerical MethodsMaster root finding algorithms such as Newton Raphson and Bisection methods, matrix solving techniques such as Gauss elimination, and iterative solvers such as Gauss Seidel.Interpolation and Curve FittingLearn various interpolation techniques and apply least squares methods for fitting data in real world modeling.Numerical Differentiation and IntegrationPerform calculus operations on discrete data using methods such as Richardson extrapolation, Simpson's rule, and Gaussian quadrature.Differential EquationsSolve ordinary differential equations using Euler, Runge Kutta, and finite difference methods with practical applications.Sampling TechniquesUnderstand and apply statistical sampling methods commonly used in surveys and experimental studies.Why Study This BookComprehensive CoverageThis book bridges the gap between statistics and numerical computation, offering a unified solution aligned with modern university syllabi.Hands on Practical LearningThrough worked examples, step by step problem solving, and software based exercises, especially using MS Excel, students develop strong computational and analytical skills.Industry Relevant ApplicationsThe techniques covered are not only examination oriented but also highly applicable in fields such as software development, data science, machine learning, scientific research, and operations management.Aligned with University CurriculumWhether you are enrolled in a technical university, central or state university, or an autonomous institution, the structure and topics closely follow standard BCA, MCA, and BTech curricula in India and internationally. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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

PAP. Zustand: 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. Zustand: new. Paperback. Unlike conventional statistics books, this book approaches statistical learning with a pragmatic, industry-relevant lens. Data is no longer confined to labs or theoretical research-it's now at the heart of product design, business decisions, artificial intelligence, and public policy. The author's goal is to empower students to not only understand statistics but also to communicate results, make data-driven decisions, and build predictive models confidently.Each topic, from central tendency to regression analysis, is backed with realistic datasets, contextual narratives, and practical scenarios. For example, while discussing logistic regression, students are guided through applications like predicting customer churn or disease detection using real-world healthcare datasets. This relevance helps learners grasp not only the what and how, but also the why behind every statistical method.Why Statistics Matters in Data ScienceStatistics is the backbone of data science. Without a good understanding of statistical methods, it's impossible to validate assumptions, interpret models, assess data quality, or make reliable predictions. In this book, the importance of statistical literacy is emphasized right from the start. The reader is encouraged to think critically, explore data patterns, and assess uncertainty-key attributes for any aspiring data professional.The book also walks students through the evolution of data science, highlighting how statistical concepts have powered machine learning, AI, and deep learning models. Even black-box algorithms require a strong grasp of statistical tools for proper tuning, validation, and error analysis.Multidisciplinary RelevanceThe book is intentionally designed to be multidisciplinary, making it a suitable companion for learners from commerce, management, life sciences, social sciences, and computer science. Whether the student is analyzing survey responses, clinical trials, market data, or customer behavior, the statistical techniques taught in this book remain universally applicable.To facilitate interdisciplinary learning, each chapter includes case studies from multiple domains. For example: Business Analytics: Sales forecasting, A/B testing, customer segmentationHealthcare: Survival analysis, clinical trial interpretationEnvironment and Agriculture: Climate data modeling, yield predictionSocial Science: Survey analysis, behavioral trend studies Curriculum-Aligned and Instructor-FriendlyThe book has been written keeping in mind the curriculum requirements of major undergraduate programs across universities. It's particularly aligned with CBCS (Choice Based Credit System) and AICTE-approved syllabi. Each unit is mapped to specific learning outcomes, making it easy for instructors to schedule classes, assign homework, and assess learning.Lecture-Wise Breakdown: Each chapter can be broken down into 4-6 lecturesAssignments & Lab Work: Ready-to-use coding exercises for Python and RMCQs and Quizzes: Objective-type questions for exam preparationMini Projects: End-of-semester projects with evaluation rubrics 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. Zustand: new. Paperback. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.