Here is a sample of data science-related books that are new to Gleeson Library. To view additional titles, see New Data Science Books on the library website.
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.
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.
Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI
“Machine Learning Q and AI” is aimed at those with foundational knowledge, and 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.
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.
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.
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.
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.
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.
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.
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.
Book summaries composed with AI-assistance.