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Books in Data

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Data Insights

  • 2nd Edition
  • April 1, 2025
  • Hunter Whitney
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 6 2 7 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 6 2 6 - 8
Data Insights: New Ways to Visualize and Make Sense of Data, Second Edition offers multi-disciplinary perspectives and useful information about how visualizations can open your eyes to data. This thought-provoking book takes a conversational approach to presenting an overview of the subject, while also focusing on key details. It highlights the ideas and work of a variety of people who are actively contributing to this still emerging field. Case studies from business analytics, healthcare, games, security, and network monitoring, among others, portray what is going on in data visualization today. A diverse blend of original illustrations and real-world examples, both classical and cutting-edge, help fill in the picture.This book provides an approachable overview of important aspects of data visualization, and: Demonstrates, with a variety of case studies, how visualizations can foster a clearer and more comprehensive understanding of data• Answers the question, How can data visualization help me? with discussions of how it fits into a wide array of purposes and situations; Makes the case that data visualization is not just about technology; it also involves a deeply human processThe second chapter of revised version of the book, the Human-Centered Design for Data Visualization focuses on two key areas affecting inclusion and diversity:· Debiasing your data and your visualizations· Neurodiversity and inclusion considerations for working with data and visualizations. Issues include: Color Blindness• Data Sonification; Multimodal data interfaces. These issues will be touched on throughout the book and will be brought up in the thought leaders interview sections. The book will explore the ways data analytics and visualization can decrease and decrease inequality.

Applied Graph Data Science

  • 1st Edition
  • February 1, 2025
  • Pethuru Raj + 4 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 9 6 5 4 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 6 5 5 - 0
Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.

Computational Intelligence for Genomics Data

  • 1st Edition
  • February 1, 2025
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 0 0 8 0 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 0 0 8 1 - 3
Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.

Data Science in the Medical Field

  • 1st Edition
  • September 25, 2024
  • Seifedine Kadry + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 0 2 8 - 7
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 0 2 9 - 4
Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications

  • 1st Edition
  • July 13, 2024
  • Siddhartha Bhattacharyya + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 5 3 3 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 5 3 2 - 1
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.

Cognitive Science, Computational Intelligence, and Data Analytics

  • 1st Edition
  • June 6, 2024
  • Vikas Khare + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 6 0 7 8 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 6 0 7 9 - 0
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.

Smart Spaces

  • 1st Edition
  • March 18, 2024
  • Fatos Xhafa + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 4 6 2 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 4 6 3 - 0
Smart Spaces covers the latest concepts and technologies surrounding smart spaces, providing technical personnel engaged in smart space related research and industries a more in-depth understanding of smart spaces. This book can be used as a reference for practicing this emerging discipline, but it will also be useful for researchers, scientists, developers, practitioners, and graduate students working in the fields of smart spaces and artificial intelligence. It combines the study of working or living spaces with computing, information equipment, and multimodal sensing devices, and with natural and convenient interactive interfaces to support how people can easily obtain services from computer systems.People's work and life in smart spaces use computer systems; it is a process of uninterrupted interaction between people and the computer system. In this process, the computer is no longer just an information processing tool that passively executes explicit human operation commands but a collaborator with people to complete tasks – a partner to human beings. International research on smart spaces is quite extensive, which shows the important role of smart spaces in ubiquitous computing research.

Object-Oriented Analysis and Design for Information Systems

  • 2nd Edition
  • March 16, 2024
  • Raul Sidnei Wazlawick
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 7 3 9 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 7 4 0 - 2
Object-Oriented Analysis and Design for Information Systems, Second Edition clearly explains real object-oriented programming in practice. Expert author Raul Sidnei Wazlawick explains concepts such as object responsibility, visibility, and the real need for delegation in detail. The object-oriented code generated by using these concepts in a systematic way is concise, organized and reusable.The patterns and solutions presented in this book are based in research and industrial applications. You will come away with clarity regarding processes and use cases and a clear understanding of how to expand a use case. Wazlawick clearly explains how to build meaningful sequence diagrams. Object-Oriented Analysis and Design for Information Systems illustrates how and why building a class model is not just placing classes into a diagram. You will learn the necessary organizational patterns so that your software architecture will be maintainable. The Second Edition includes all new content shifting the focus of the book to agile software development, including Scrum software project management, BPMN diagrams, user stories, and Python code examples.

Data Fusion Techniques and Applications for Smart Healthcare

  • 1st Edition
  • March 12, 2024
  • Amit Kumar Singh + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 2 3 3 - 9
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 2 3 4 - 6
Data Fusion Techniques and Applications for Smart Healthcare covers cutting-edge research from both academia and industry, with a particular emphasis on recent advances in algorithms and applications that involve combining multiple sources of medical information. The book can be used as a reference for practicing engineers, scientists, and researchers, but it will also be useful for graduate students and practitioners from government and industry as well as healthcare technology professionals working on state-of-the-art information fusion solutions for healthcare applications.Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images, 3D volumes, or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make auscultation reports in text format; electrocardiograms can be printed in time series format, X-rays saved as images; volume can be provided through angiography; temporal information by echocardiograms, and 4D information extracted through flow MRI.

Synthetic Data and Generative AI

  • 1st Edition
  • January 9, 2024
  • Vincent Granville
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 8 5 7 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 8 5 6 - 9
Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.

Related subjects

Coding and information theory

Data encryption

Data general

Data storage representations

Data structures