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Books in Data general

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Data-Driven Insights and Analytics for Measurable Sustainable Development Goals

  • 1st Edition
  • June 2, 2025
  • Tilottama Goswami + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 3 0 4 4 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 3 0 4 5 - 2
As pursuit of the Sustainable Development Goals (SDGs) becomes central to our global mission, there is a growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues effectively, one should be able to measure and analyze progress. Data Driven Insights and Analytics for Measurable Sustainable Development Goals bridges the gap between qualitative and quantitative assessments, recognizing that the SDGs are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how the 5 Parts of SDGs intersect and influence each other, providing a holistic perspective on sustainable development. The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance, and thus offering valuable insights into where we stand on these critical goals. In addition, to make informed decisions and drive the SDGs forward, readers need more than just an understanding of the current situation. The book demonstrates the need for prescriptive analytics, a methodology that guides actions. Prescriptive analytics provides actionable recommendations based on data analysis, helping readers to identify specific interventions and policies that can accelerate progress. Furthermore, predictive analytics is vital in anticipating future trends and challenges. By showing how readers can forecast potential obstacles and opportunities, the book presents tools for taking proactive measures to navigate our path toward the SDGs effectively. By offering a data-driven approach, the book ensures that every action taken is based on sound analysis, making a tangible difference in building a better and safer world for all.

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.

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.

Mathematical Methods in Data Science

  • 1st Edition
  • January 6, 2023
  • Jingli Ren + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 8 6 7 9 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 8 6 8 0 - 6
Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.

Meeting the Challenges of Data Quality Management

  • 1st Edition
  • January 25, 2022
  • Laura Sebastian-Coleman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 1 7 3 7 - 5
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 1 7 5 6 - 6
Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly.   The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage.   This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.