New Books
Explorations in the Mathematics of Data Science: The Inaugural Volume of the Center for Approximation and Mathematical Data Analytics
This edited volume reports on the recent activities of the new Center for Approximation and Mathematical Data Analytics (CAMDA) at Texas A&M University. Chapters are based on talks from CAMDA's inaugural conference - held in May 2023 - and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those rooted in approximation theory.
Responsible Data Science
"Responsible Data Science" addresses critical ethical issues in the field, focusing on the unintended consequences of opaque algorithms. It highlights cases of bias, injustice, and discrimination resulting from widespread "black box" algorithms. The book offers practical guidance for data scientists and managers to implement ethical solutions, minimize harm to vulnerable groups, and improve model transparency. It covers methods to diagnose bias, ensure fairness, and audit projects for unintended consequences. This resource is essential for data science practitioners, managers, software developers, and statisticians seeking to navigate the ethical challenges of modern data science.
The Decision Maker's Handbook to Data Science: AI and Data Science for Non-Technical Executives, Managers, and Founders
This updated edition of "The Decision Maker's Handbook to Data Science" by Stylianos Kampakis explores the latest advancements in AI, particularly large language models, and their impact on various industries. The book emphasizes the importance of understanding data science and AI for decision-makers, highlighting the distinctions between AI and traditional data science. It delves into crucial topics such as ethics in AI, including bias, fairness, and accountability. Kampakis provides guidance on developing effective data strategies, avoiding common pitfalls, and building a data-driven culture within organizations. The book also covers hiring and managing data scientists, project assessment, and includes case studies to illustrate key concepts. It aims to bridge the communication gap between management and data scientists, making it an essential guide for non-technical decision-makers and those seeking an introduction to data science.
Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks
"Gradient Expectations" by Keith L. Downing explores the predictive functions of neural networks and their potential to advance AI. The book investigates the similarities between natural and artificial neural networks, focusing on how prediction mechanisms evolved in mammalian brains. Downing examines computational models that utilize predictive mechanisms with biological plausibility, highlighting the role of gradients in both natural and artificial networks. By synthesizing research from neuroscience, cognitive science, and connectionism, the book offers a comprehensive perspective on predictive neural-network models and proposes integrating computational prediction models with evolutionary algorithms to enhance AI capabilities.
The Age of Prediction: Algorithms, AI, and the Shifting Shadows of Risk
"The Age of Prediction" explores the rapid advancement of AI and big data in enhancing predictive capabilities across various fields, from investing to medicine. The book examines how these technologies are reshaping our world, but also highlights the paradoxical effects of improved predictions on risk perception and behavior. It discusses how increased predictability can lead to complacency or unintended consequences, such as less attentive driving due to GPS reliance. The authors question whether risk can be eliminated entirely and investigate how narrower risks might impact markets, insurance, and risk tolerance. The book showcases novel cross-disciplinary tools used for predictions in fields like cancer research and stock dynamics.
Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI
"Machine Learning and AI Beyond the Basics" is a concise guide addressing 30 essential questions in the field. Aimed at those with foundational knowledge, it covers advanced topics in deep neural networks, computer vision, NLP, deployment, and model evaluation. The book offers practical insights on reducing overfitting, handling randomness, optimizing inference, and applying cutting-edge concepts like the lottery ticket hypothesis. It also explores self-attention, data augmentation, self-supervised learning, and generative AI. This resource helps practitioners stay current with the latest technologies and prepare for technical interviews, all without requiring code execution or proof-solving.
Statistics for Data Science and Analytics
Statistics for Data Science and Analytics is a comprehensive Python-based textbook for statistical analysis in data science. It covers essential topics like prediction, correlation, and data exploration, introducing statistical concepts and their Python implementations. The book includes hypothesis testing, probability, exploratory data analysis, A/B testing, binary classification, and regression. It emphasizes practical applications, using resampling and bootstrap methods for inference. Each chapter provides examples, explanations, and Python code snippets. The text is designed for data science instructors and students, offering a solid foundation in statistics and its application in the field.
Cultures of Prediction: How Engineering and Science Evolve with Mathematical Tools
"Cultures of Prediction" explores the evolution of predictive methods in science and engineering over four centuries. Authors Johnson and Lenhard identify four predictive cultures: rational, empirical, iterative-numerical, and exploratory-iterative. They argue that mathematization in prediction is multifaceted, not a uniform process. The book examines pre-computer and computer-age prediction, highlighting how different modes coevolved with technology. This shift challenges traditional views of scientific theories as primarily explanatory, influencing research priorities and funding. The authors emphasize that prediction's history is not a simple triumph of abstract mathematics, but a complex interplay of various predictive cultures.
Mitigating Bias in Machine Learning
"Mitigating Bias in Machine Learning" is a comprehensive guide that addresses the critical issue of bias in AI systems. The book provides practical strategies to reduce discrimination based on factors like ethnicity and gender across various AI applications. It features contributions from experts in the field, covering topics such as ethical implications, social media, healthcare, natural language processing, and large language models. Through real-world case studies, the authors demonstrate how to identify and mitigate biases in machine learning systems, promoting fairness and equity in AI development and deployment across different industries.
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.