New Books
Strategic Blueprint for Enterprise Analytics: Integrating Advanced Analytics into Data-Driven Business
This comprehensive guide explores the implementation of enterprise analytics (EA) systems in large organizations. Divided into three parts, it covers adaptable architecture, technical considerations, and strategy execution. The book addresses data, cloud platforms, AI solutions, investment, and risk management. It offers insights for professionals, leaders, and academics on harnessing data's potential to foster growth and optimize operations. Readers will gain knowledge to navigate the dynamic world of EA, enabling them to create robust analytics capabilities and drive their organizations toward a data-driven future.
Data Science: the Hard Parts: Techniques for Excelling at Data Science
This practical guide offers often-overlooked techniques and best practices in data engineering and data science. It challenges the notion that expertise in machine learning and programming alone makes a great data scientist. Instead, it emphasizes the importance of smaller tools and skills that truly distinguish exceptional data scientists. The book covers various topics, including value creation in data science, compelling project narratives, business case building, feature creation for ML models, KPI decomposition, and growth analysis. Written by Daniel Vaughan, head of data at Clip and author of "Analytical Skills for AI and Data Science," this guide aims to bridge the gap between average candidates and qualified working data scientists.
The Music in the Data: Corpus Analysis, Music Analysis, and Tonal Traditions
"The Music in the Data" proposes a novel humanities-based approach to analyzing big data in music research. The author argues that large music datasets can be both objectively analyzed and subjectively interpreted like texts, offering new insights into musical traditions. The book explores core music theory topics through quantitative analysis of large datasets combined with qualitative interpretation. It introduces basic data analysis techniques while connecting empirical information with theories of musical meaning. This approach bridges the gap between data-driven and traditional music research methods, providing a valuable perspective for scholars and students in various music-related fields. The book won the 2023 Emerging Scholar Award from the Society for Music Theory.
Big Data and the Welfare State: How the Information Revolution Threatens Social Solidarity
The book examines how the rise of "big data" challenges traditional welfare state principles. While welfare states historically operated on the assumption of universal social insurance due to limited information about individual risks, modern data analytics can now precisely assess personal risk levels. This technological shift is polarizing preferences for public insurance and leading to market segmentation, where insurance pools become smaller and less redistributive. The authors demonstrate these effects through analyses of health insurance, unemployment benefits, life insurance, and credit markets, showing how data abundance is fundamentally reshaping social protection politics.
Distrust: Big Data, Data-Torturing, and the Assault on Science
There is no doubt science is currently suffering from a credibility crisis. This thought-provoking book argues that, ironically, science's credibility is being undermined by tools created by scientists themselves. Scientific disinformation and damaging conspiracy theories are rife because of the internet that science created, the scientific demand for empirical evidence and statistical significance leads to data torturing and confirmation bias, and data mining is fuelled by the technological advances in Big Data and the development of ever-increasingly powerful computers. Using a wide range of entertaining examples, this fascinating book examines the impacts of society's growing distrust of science, and ultimately provides constructive suggestions for restoring the credibility of the scientific community.
Ethical Data Science: Prediction in the Public Interest
Anne L. Washington's "Ethical Data Science" explores the challenges of using data science for public good. The book argues that predictive technologies often prioritize financial interests over societal benefits, embedding administrative preferences that neglect marginalized groups. Washington introduces the "prediction supply chain" to highlight ethical concerns alongside legal and commercial influences. By examining the data science workflow, the book encourages critical thinking about the human impact of data-driven decisions. It provides a framework for practitioners, academics, policymakers, and others to identify social dynamics in data trends and develop more inclusive approaches to data science.
Data Smart: Using Data Science to Transform Information into Insight
"Data Smart: Using Data Science to Transform Information into Insight" by Jordan Goldmeier demystifies data science, presenting it as an approachable problem-solving method rather than a complex technological feat. This updated edition teaches readers how to implement data science concepts using Microsoft Excel, making it accessible to a wide audience. The book covers statistics, machine learning, and AI, providing practical tutorials, colorful visualizations, and real-world applications. It aims to equip students, analysts, and managers with the skills to tackle data challenges and share insights effectively, all within the familiar environment of a spreadsheet.
Handbook of Big Data Research Methods
This comprehensive Handbook explores the impact of big data analytics across various business sectors, including finance, healthcare, and telecommunications. It offers innovative approaches to overcome challenges in big data research and proposes new directions using descriptive, diagnostic, predictive, and prescriptive analytics. The book covers topics such as fraud detection, disease identification, customer loyalty enhancement, and the use of artificial intelligence in accounting. It also discusses the implementation of data science platforms and the role of public cloud in creating successful ecosystems. The Handbook serves as a valuable resource for students, scholars, and professionals in business analytics, economics, and related fields.
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
In "Weapons of Math Destruction," Cathy O'Neil warns about the pervasive use of mathematical models in decision-making processes. These algorithms, often opaque and unregulated, can reinforce discrimination and widen societal gaps. O'Neil argues that instead of promoting fairness, these models can trap individuals in vicious cycles, particularly affecting the underprivileged. She examines how these "weapons of math destruction" impact various aspects of life, from education and employment to credit and criminal justice. O'Neil calls for increased accountability from modelers, stricter regulation, and greater public awareness to challenge and change these potentially harmful systems.
The SAGE Encyclopedia of Theory in Science, Technology, Engineering, and Mathematics
This encyclopedia explores the role of theories in STEM disciplines, examining how they shape understanding and learning in these fields. It delves into the construction, evolution, and significance of theories, highlighting their importance in unlocking the mysteries of the world. The work features over 200 expert-authored articles, organized thematically with a Reader's Guide. Each entry includes further readings, cross-references, and a Resource Guide listing key books, journals, associations, and websites. This comprehensive reference provides valuable insights into the theoretical foundations of science, technology, engineering, and mathematics.
Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation
This book explores the convergence of cloud computing, artificial intelligence, and Big Data, examining their foundations, applications, and synergies. It covers 17 chapters detailing the interplay of these next-generation paradigms and their hybrid blend with IoT. The text discusses recent advancements, applications, and challenges in areas such as security, latency, energy consumption, and healthcare services. It provides a comprehensive overview of state-of-the-art applications, proposed solutions, and framework enhancements. The book is aimed at researchers, postgraduate students, and professionals in computing, software engineering, electrical engineering, data analysis, and cybersecurity.