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85 results in Knowledge Management, Databases and Data Mining

Page 1 of 5

Blockchain Fundamentals, Data Structures, and Algorithms for Data Science
  • Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
  • Coming soon
  • Expected online publication date:
    August 2026
    Print publication:
    31 August 2026
  • Offering a systematic exploration of blockchain networks from both technical and analytical viewpoints, this book introduces the core structures that underpin blockchain systems, transactions, addresses, and smart contracts and explains how these can be modeled, visualized, and analyzed using modern data science methods. Bridging computer science, finance, and statistics, it integrates algorithmic reasoning with economic intuition to study decentralization, risk, and trust in digital economies. Through examples drawn from Bitcoin, Ethereum, Ripple, Monero, Zcash, IOTA, and DeFi, readers learn how blockchain data can be transformed into graph and temporal models for fraud detection, systemic risk analysis, and network behavior prediction. Featuring clear explanations, illustrative figures, and Solidity code, this volume serves as an essential reference for students, researchers, and practitioners in finance, data science, statistics, machine learning, and distributed systems.

Data Analytics using Python

Doing digital history
  • A beginner’s guide to working with text as data
  • Jonathan Blaney, Sarah Milligan, Marty Steer, Jane Winters
  • Published by:
    Manchester University Press
    Published online:
    28 February 2026
  • This book offers a practical introduction to digital history with a focus on working with text. It will benefit anyone who is considering carrying out research in history that has a digital or data element and will also be of interest to researchers in related fields within digital humanities, such as literary or classical studies. It offers advice on the scoping of a project, evaluation of existing digital history resources, a detailed introduction on how to work with large text resources, how to manage digital data and how to approach data visualisation. After placing digital history in its historiographical context and discussing the importance of understanding the history of the subject, this guide covers the life-cycle of a digital project from conception to digital outputs. It assumes no prior knowledge of digital techniques and shows you how much you can do without writing any code. It will give you the skills to use common formats such as plain text and XML with confidence. A key message of the book is that data preparation is a central part of most digital history projects, but that work becomes much easier and faster with a few essential tools.

A Hands-On Introduction to Data Science with R
  • 2nd edition
  • Chirag Shah
  • Published online:
    07 February 2026
    Print publication:
    22 January 2026
  • Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool R, a new chapter on using R for statistical analysis, and a new chapter that demonstrates how to use R within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.

A Hands-On Introduction to Data Science with Python
  • 2nd edition
  • Chirag Shah
  • Published online:
    07 February 2026
    Print publication:
    22 January 2026
  • Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Insight-Driven Problem Solving
  • Analytics Science to Improve the World
  • Soroush Saghafian
  • Published online:
    21 October 2025
    Print publication:
    30 October 2025
  • Improving public policies, creating the next generation of AI systems, reducing crime, making hospitals more efficient, addressing climate change, controlling pandemics, and reducing disruption in supply chains are all problems where big picture ideas from analytics science have had large-scale impact. What are those ideas? Who came up with them? Will insights from analytics science help solve even more daunting societal challenges? This book takes readers on an engaging tour of the evolution of analytics science and how it brought together ideas and tools from many different fields – AI, machine learning, data science, OR, optimization, statistics, economics, and more – to make the world a better place. Using these ideas and tools, big picture insights emerge from simplified settings that get at the essence of a problem, leading to superior approaches to complex societal issues. A fascinating read for anyone interested in how problems can be solved by leveraging analytics.

The AI and Data Revolution
  • Understanding the New Data Landscape
  • Martin De Saulles
  • Published by:
    Facet
    Published online:
    20 August 2025
    Print publication:
    24 April 2025
  • We are entering a new phase in the information revolution driven by the introduction of innovative artificial intelligence (AI) technologies. From the rise of mass media, mass communications and the expansion of the internet, to mobile computing, social networks and Generative AI, this important and authoritative book outlines the key changes over the last thirty years that have led to this moment.

    Drawing on established frameworks, theories, historical research and empirical evidence, this book argues that the current wave of AI-driven innovations represents a step-change in how organisations can extract value from data and that this will have significant implications for business innovation and how companies compete. Individual chapters explore (a) the history of the information industry and key milestones in artificial intelligence, (b) an overview of the data and AI landscape, (c) the opportunities and challenges of the AI revolution, (d) the ethical, policy and legal issues of data-driven AI, (e) and scenarios for where the data revolution is heading up to 2030.

Probability and Statistics for Data Science
  • Carlos Fernandez-Granda
  • Published online:
    19 June 2025
    Print publication:
    03 July 2025
  • This self-contained guide introduces two pillars of data science, probability theory, and statistics, side by side, in order to illuminate the connections between statistical techniques and the probabilistic concepts they are based on. The topics covered in the book include random variables, nonparametric and parametric models, correlation, estimation of population parameters, hypothesis testing, principal component analysis, and both linear and nonlinear methods for regression and classification. Examples throughout the book draw from real-world datasets to demonstrate concepts in practice and confront readers with fundamental challenges in data science, such as overfitting, the curse of dimensionality, and causal inference. Code in Python reproducing these examples is available on the book's website, along with videos, slides, and solutions to exercises. This accessible book is ideal for undergraduate and graduate students, data science practitioners, and others interested in the theoretical concepts underlying data science methods.

Tensor Decompositions for Data Science
  • Grey Ballard, Tamara G. Kolda
  • Published online:
    05 June 2025
    Print publication:
    26 June 2025
  • Tensors are essential in modern day computational and data sciences. This book explores the foundations of tensor decompositions, a data analysis methodology that is ubiquitous in machine learning, signal processing, chemometrics, neuroscience, quantum computing, financial analysis, social science, business market analysis, image processing, and much more. In this self-contained mathematical, algorithmic, and computational treatment of tensor decomposition, the book emphasizes examples using real-world downloadable open-source datasets to ground the abstract concepts. Methodologies for 3-way tensors (the simplest notation) are presented before generalizing to d-way tensors (the most general but complex notation), making the book accessible to advanced undergraduate and graduate students in mathematics, computer science, statistics, engineering, and physical and life sciences. Additionally, extensive background materials in linear algebra, optimization, probability, and statistics are included as appendices.

Probabilistic Data-Driven Modeling
  • Tomaso Aste
  • Published online:
    17 May 2025
    Print publication:
    01 May 2025
  • This book introduces relevant and established data-driven modeling tools currently in use or in development, which will help readers master the art and science of constructing models from data and dive into different application areas. It presents statistical tools useful to individuate regularities, discover patterns and laws in complex datasets, and demonstrates how to apply them to devise models that help to understand these systems and predict their behaviors. By focusing on the estimation of multivariate probabilities, the book shows that the entire domain, from linear regressions to deep learning neural networks, can be formulated in probabilistic terms. This book provides the right balance between accessibility and mathematical rigor for applied data science or operations research students, graduate students in CSE, and machine learning and uncertainty quantification researchers who use statistics in their field. Background in probability theory and undergraduate mathematics is assumed.

Deep Learning Recommender Systems
  • Zhe Wang, Chao Pu, Felice Wang
  • Published online:
    08 May 2025
    Print publication:
    22 May 2025
  • Recommender systems are ubiquitous in modern life and are one of the main monetization channels for Internet technology giants. This book helps graduate students, researchers and practitioners to get to grips with this cutting-edge field and build the thorough understanding and practical skills needed to progress in the area. It not only introduces the applications of deep learning and generative AI for recommendation models, but also focuses on the industry architecture of the recommender systems. The authors include a detailed discussion of the implementation solutions used by companies such as YouTube, Alibaba, Airbnb and Netflix, as well as the related machine learning framework including model serving, model training, feature storage and data stream processing.

Data, Systems, and Society
  • Harnessing AI for Societal Good
  • Munther A. Dahleh
  • Published online:
    24 March 2025
    Print publication:
    27 March 2025
  • Harnessing the power of data and AI methods to tackle complex societal challenges requires transdisciplinary collaborations across academia, industry, and government. In this compelling book, Munther A. Dahleh, founder of the MIT Institute for Data, Systems, and Society (IDSS), offers a blueprint for researchers, professionals, and institutions to create approaches to problems of high societal value using innovative, holistic, data-driven methods. Drawing on his experience at IDSS and knowledge of similar initiatives elsewhere, Dahleh describes in clear, non-technical language how statistics, data science, information and decision systems, and social and institutional behavior intersect across multiple domains. He illustrates key concepts with real-life examples from optimizing transportation to making healthcare decisions during pandemics to understanding the media's impact on elections and revolutions. Dahleh also incorporates crucial concepts such as robustness, causality, privacy, and ethics and shares key lessons learned about transdisciplinary communication and about unintended consequences of AI and algorithmic systems.

The Value of Industrial Relations
  • Contemporary Work and Employment in Britain
  • Edited by Andy Hodder, Stephen Mustchin
  • Published by:
    Bristol University Press
    Published online:
    07 January 2025
    Print publication:
    15 January 2024
  • Published in collaboration with The British Universities Industrial Relations Association (BUIRA), this book critically reviews the future of Industrial Relations (IR) in a changing work landscape and traces its historical evolution. Essential for academics, students and trade unions, it explores IR's significant changes over the past decade and its ongoing influence on our lives.

Crises at Work
  • Economy, Climate and Pandemic
  • Steve Williams, Mark Erickson
  • Published by:
    Bristol University Press
    Published online:
    27 December 2024
    Print publication:
    06 September 2024
  • It is impossible to view the news at present without hearing talk of crisis: the economy, the climate, the pandemic. This book asks how these larger societal issues lead to a crisis with work, making it ever more precarious, unequal and intense. Experts diagnose the nature of the problem and offer a programme for transcending above the crises.

Menopause Transitions and the Workplace
  • Theorizing Transitions, Responsibilities and Interventions
  • Edited by Vanessa Beck, Jo Brewis
  • Published by:
    Bristol University Press
    Published online:
    17 December 2024
    Print publication:
    10 January 2024
  • Offering theoretical frameworks from experts as well as practical examples to support women transitioning through menopause in the workplace, this is a go-to reference for academics and policy makers working in the field.

Introduction to Probability and Statistics for Data Science
  • with R
  • Steven E. Rigdon, Ronald D. Fricker, Jr, Douglas C. Montgomery
  • Published online:
    13 December 2024
    Print publication:
    14 November 2024
  • Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises.

Data Management
  • Interactions with Computer Architecture and Systems
  • Xiaodong Zhang, Rubao Lee
  • Published online:
    21 November 2024
    Print publication:
    28 November 2024
  • This guide illuminates the intricate relationship between data management, computer architecture, and system software. It traces the evolution of computing to today's data-centric focus and underscores the importance of hardware-software co-design in achieving efficient data processing systems with high throughput and low latency. The thorough coverage includes topics such as logical data formats, memory architecture, GPU programming, and the innovative use of ray tracing in computational tasks. Special emphasis is placed on minimizing data movement within memory hierarchies and optimizing data storage and retrieval. Tailored for professionals and students in computer science, this book combines theoretical foundations with practical applications, making it an indispensable resource for anyone wanting to master the synergies between data management and computing infrastructure.

Working with Network Data
  • A Data Science Perspective
  • James Bagrow, Yong‐Yeol Ahn
  • Published online:
    06 June 2024
    Print publication:
    13 June 2024
  • Drawing examples from real-world networks, this essential book traces the methods behind network analysis and explains how network data is first gathered, then processed and interpreted. The text will equip you with a toolbox of diverse methods and data modelling approaches, allowing you to quickly start making your own calculations on a huge variety of networked systems. This book sets you up to succeed, addressing the questions of what you need to know and what to do with it, when beginning to work with network data. The hands-on approach adopted throughout means that beginners quickly become capable practitioners, guided by a wealth of interesting examples that demonstrate key concepts. Exercises using real-world data extend and deepen your understanding, and develop effective working patterns in network calculations and analysis. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.

Employer Engagement
  • Making Active Labour Market Policies Work
  • Edited by Jo Ingold, Patrick McGurk
  • Published by:
    Bristol University Press
    Published online:
    18 January 2024
    Print publication:
    28 February 2023
  • Active labour market policies aim to assist people not in work into work through a range of interventions including job search, training and in-work support and development. While policies and scholarship predominantly focus on jobseekers' engagement with these initiatives, this book sheds light for the first time on the employer's perspective.

Halo Data
  • Understanding and Leveraging the Value of your Data
  • Caroline Carruthers, Peter Jackson
  • Published by:
    Facet
    Published online:
    17 December 2023
    Print publication:
    14 September 2023
  • The past two decades have seen an explosion both in the volume of data we use, and our understanding of its management. However, while techniques and technology for manipulating data have advanced rapidly in this time, the concepts around the value of our data have not. This lack of progress has made it increasingly difficult for organisations to understand the value in their data, the value of their data, and how to exploit that value.

    Halo Data proposes a paradigm shift in methodology for organisations to properly appreciate and leverage the value of their data. Written by an author team with many years' experience in data strategy, management and technology, the book will first review the current state of our understanding of data. This opening will demonstrate the limitations of this status quo, including a discussion on metadata and its limitations, data monetisation and data-driven business models. Following this, the book will present a new concept and framework for understanding and quantifying value in an organisation's data and a practical methodology for using this in practice.

    Ideal for data leaders and executives who are looking to leverage the data at their fingertips.

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