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Books in Data structures

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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.

Computational Intelligence for Genomics Data

  • 1st Edition
  • February 1, 2025
  • Babita Pandey + 4 more
  • 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.

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.

Data Analytics for Social Microblogging Platforms

  • 1st Edition
  • November 4, 2022
  • Soumi Dutta + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 1 7 8 5 - 8
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 7 2 3 0 - 7
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

Data Simplification

  • 1st Edition
  • March 9, 2016
  • Jules J. Berman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 7 8 1 - 2
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 8 5 4 - 3
Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.

Data Mapping for Data Warehouse Design

  • 1st Edition
  • December 8, 2015
  • Qamar Shahbaz
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 5 1 8 5 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 5 3 3 5 - 5
Data mapping in a data warehouse is the process of creating a link between two distinct data models’ (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle.

Repurposing Legacy Data

  • 1st Edition
  • March 13, 2015
  • Jules J. Berman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 8 8 2 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 9 1 5 - 2
Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain. Most of the data stored worldwide is legacy data—data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it. The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.

Principles of Artificial Intelligence

  • 1st Edition
  • June 28, 2014
  • Nils J. Nilsson
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 5 8 6 - 2
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.