Skip to main content
View Cart

Books in Data

This collection spans data management, storage, retrieval, and big data analytics. Showcasing advanced database technologies, data science, and data mining, it supports researchers, data scientists, and industry professionals in harnessing data for insights. Addressing data quality, privacy, and scalable architectures, these resources enable informed decision-making and foster innovation in data-driven domains.

  • A Comprehensive Guide to R Programming for Data Analytics

    • 1st Edition
    • October 1, 2026
    • Parul Acharya
    • English
    A Comprehensive Guide to R Programming for Data Analytics provides a comprehensive presentation of univariate and multivariate statistical models within the general linear model and generalized linear model framework to analyze simple and complex data using R software. This book presents popular R packages that are used in data mining (e.g., caret-classification and regression, lubridate-dates and times, string-R for string data) and visualization (e.g., ggplot, ggthemes, ggtext). The R packages used to analyze data using a particular statistical model are explained through real-world and publicly available datasets. R codes are presented in a manner that helps readers understand the program code syntax.Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn R programming to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues in R syntax, as well as definitions of key terms. Checkpoints are included to recap the concepts learned in each chapter. The book helps readers enhance their conceptual understanding and practical application of statistical models to real-world datasets, and enables readers to gain competency in R programming, which is an important skill in today’s data-driven market.

  • Data Compression for Data Mining Algorithms

    • 1st Edition
    • May 29, 2026
    • Xiaochun Wang
    • English
    Data Compression for Data Mining Algorithms tackles the important problems in the design of more efficient data mining algorithms by way of data compression techniques and provides the first systematic and comprehensive description of the relationships between data compression mechanisms and the computations involved in data mining algorithms. Data mining algorithms are powerful analytical techniques used across various disciplines, including business, engineering, and science. However, in the big data era, tasks such as association rule mining and classification often require multiple scans of databases, while clustering and outlier detection methods typically depend on Euclidean distance for similarity measures, leading to high computational costs.Data Compression for Data Mining Algorithms addresses these challenges by focusing on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations involved in tasks such as feature selection and similarity computation. The book features the latest developments in both lossless and lossy data compression methods and provides a comprehensive exposition of data compression methods for data mining algorithm design from multiple points of view.Key discussions include Huffman coding, scalar and vector quantization, transforms, subbands, wavelet-based compression for scalable algorithms, and the role of neural networks, particularly deep learning, in feature selection and dimensionality reduction. The book’s contents are well-balanced for both theoretical analysis and real-world applications, and the chapters are well organized to compose a solid overview of the data compression techniques for data mining. To provide the reader with a more complete understanding of the material, projects and problems solved with Python are interspersed throughout the text.

  • Digital Twins for Sustainable Development

    • 1st Edition
    • April 1, 2026
    • Valentina Emilia Balas + 4 more
    • English
    Digital Twins for Sustainable Development covers digital twins for sustainability as a virtual representation of a physical system or environment, such as a building, city, or natural ecosystem and how they are used to support sustainable development and management practices. The book demonstrates how data from a variety of sources, such as sensors, satellite imagery, and other monitoring tools can be used for advanced analytics and modeling techniques to simulate the system's behavior over time. This allows researchers and professionals in computer science to manage complex systems and promote sustainable development and resource management practices.

  • Quantum Cryptography and Annealing for Securing Industrial IoT

    • 1st Edition
    • February 27, 2026
    • Seifedine Kadry + 5 more
    • English
    Quantum Cryptography and Annealing for Securing Industrial IoT focuses on the rapidly evolving field of quantum security solutions for Industrial Internet of Things (IIoT) platforms, emphasizing the critical intersection of quantum cryptography, post-quantum cryptography, and their practical applications in IIoT. The book’s primary objective is to drive advancements that significantly intersect quantum cryptography in securing IIoT devices, elevate secure IIoT infrastructures, and optimize the overall delivery. Distinguishing itself by prioritizing practical applications, it offers a nuanced perspective on how technological integrations in quantum cryptosystems are actively employed in real-world scenarios. The authors meticulously examine the role of quantum cryptosystems in the design, analysis, and optimization of IIoT-specific hardware, covering their resilience to physical and side-channel attacks and evaluating performance. This book strikes a balance between theoretical concepts and practical applications, providing insights into the challenges and solutions encountered in applying quantum cryptographical principles to IIoT engineering problems.

  • Foundations of Cloud Computing

    • 1st Edition
    • February 15, 2026
    • Robert Shimonski
    • English
    Foundations of Cloud Computing provides readers with a guidebook to navigating the field of Cloud Computing, including the guiding principles, key concepts, history, terminology, state-of-the-art in research, and a roadmap to where the field intersects and interacts with related fields of research and development. In this age of total connectivity, researchers need to be able to communicate and collaborate with a wide range of colleagues across multiple disciplines. This book helps researchers from all fields understand what Cloud Computing is, how it works, and how to speak the language of collaboration with developers and researchers who specialize in the field. With a complete and in-depth foundation in the key concepts of the field, and a roadmap to where and how Cloud Computing intersects across the domains of scientific research and application development, this book gives readers everything they need to navigate and apply this important, ubiquitous technology.

  • Fundamentals of Statistics for Researchers and Data Analysts

    • 1st Edition
    • January 23, 2026
    • Shashi A. Chiplonkar + 1 more
    • English
    Fundamentals of Statistics for Researchers and Data Analysts explains statistical methods, and the assumptions and prerequisites for applying various analytical tools from an statistical point of view. Statistical analysis has become indispensable in almost all fields of science, business, industry and medicine, for evidence-based decision making and forecasting. However, due to lack of fundamental understanding of statistics, results of data analysis often remain inconclusive or erroneous. In addition, data analysts or even statistical advisers may not be familiar with the subject area of the data, leading to inaccurate application of statistical tools and interpretation of results. To address these issues, this book breaks down the concepts of statistics into accessible, practical explanations with real-world examples. The book is organized by first explaining what statistical thinking is and how one should proceed with formulating their question in terms of a statistical hypothesis. Then step by step, topics are explained in detail, including data generation by choice of proper study design, data collection methods, identifying outliers, methods of data analysis, and finally interpretation of results to help make the required decision. Essential statistical methods such as classification techniques, correlation analysis, regression models, probability distributions, model building and statistical tests of significance are explained with live datasets using Excel and SPSS. Fundamentals of Statistics for Researchers and Data Analysts instructs readers on the precise methodology of analyzing data and interpretation of statistical results to arrive at a valid conclusion. Readers can use the same methodology from the case studies given in the book for their own applications and research by replacing the variables in the examples with the variables from their own datasets. The book ensures that readers are well-prepared for data-driven roles in various sectors.

  • Quantum Theory, Decision Making and Social Dynamics

    • 1st Edition
    • January 8, 2026
    • Tofigh Allahviranloo + 3 more
    • English
    Quantum Theory, Decision Making, and Social Dynamics is a detailed exploration of the connection between quantum theory, decision-making, and social networks. As quantum theory expands into various fields, there is an increasing demand for accessible resources that clarify its principles and uses. This book aims to address that need by explaining the complex relationship between quantum theory and social dynamics, especially in decision-making contexts. It discusses the challenges of understanding and applying quantum theory in social settings and provides readers with the knowledge to leverage its potential in decision-making processes. The book is divided into eleven chapters, each focusing on a specific aspect of quantum theory and its applications. Chapter 1 introduces quantum theory, fuzzy logic, and social network analysis, highlighting key concepts like superposition, entanglement, and fuzzy influence within networks. Chapter 2 examines fuzzy sets, membership functions, and inference systems, with applications in devices, traffic management, and healthcare. Chapter 3 covers the mathematical framework of quantum mechanics and its philosophical paradoxes, connecting them to fuzzy logic models of uncertainty. Chapter 4 links social networks to quantum graphs, defining their topology, centrality, and entangled edges. Chapter 5 models social identity as a fuzzy quantum superposition, exploring identity collapse and coherence within networks. Chapter 6 relates quantum entanglement to social ties, proposing fuzzy–quantum graph models for interconnected systems. Chapter 7 analyses measures of irregularity in quantum graphs and applies these to financial networks. Chapter 8 integrates quantum cognition with fuzzy MCDM, employing various probability evaluation methods. Chapter 9 features case studies of fuzzy systems and their integration with quantum fuzzy graphs. Chapter 10 develops a quantum graph-based link prediction model for dynamic social networks. Chapter 11 concludes with a summary of the quantum–fuzzy framework, discussing its contributions, limitations, and future directions.

  • Essentials of Big Data Analytics

    Applications in R and Python
    • 1st Edition
    • January 6, 2026
    • Pallavi Chavan + 2 more
    • English
    Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments.

  • Mathematical Modeling for Big Data Analytics

    • 1st Edition
    • November 17, 2025
    • Passent El-Kafrawy + 1 more
    • English
    Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

  • Data Science for Teams

    20 Lessons from the Fieldwork
    • 1st Edition
    • July 30, 2025
    • Harris V. Georgiou
    • English
    Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.

Related subjects