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.
Tangles: A Structural Approach to Artificial Intelligence in the Empirical Sciences
“Tangles: A Structural Approach to Artificial Intelligence in the Empirical Sciences” introduces a novel mathematical framework for identifying patterns in complex data. Tangles group related qualities, revealing clusters and types across diverse fields like politics, health, and biology. This structural approach to AI offers new ways to understand, classify, and predict complex phenomena. The book explores applications ranging from data science and machine learning to economics, genetics, and text analysis. By making the recently axiomatized theory of tangles accessible to a broad scientific audience, the book demonstrates its potential to revolutionize data analysis across multiple disciplines.
The Data Science Handbook“The Data Science Handbook” offers a comprehensive, practical guide to becoming a data scientist. It covers essential skills including mathematics, software engineering, business understanding, and data analysis. The book emphasizes real-world applications over theoretical concepts, providing sample code and library discussions. It also addresses the practical aspects of working in data science, including project lifecycles and organizational roles. Updated for its 2nd edition, it incorporates recent developments in AI, such as Large Language Models, and the emergence of ML Engineering. The book caters to aspiring data scientists and professionals seeking to leverage analytics in their organizations, reflecting the evolving nature of the field.
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.
Practical Machine Learning: A Beginner’s Guide with Ethical Insights
This book offers a beginner-friendly introduction to machine learning, covering fundamental skills and techniques for real-world applications. It guides readers through data handling, model development, and deployment across various domains. The text emphasizes responsible and explainable AI integration, prioritizing ethical considerations. It provides access to additional resources like datasets, libraries, and pre-trained models. As an Open Access resource, it serves as a core text for students and instructors in machine learning and data science, combining practical knowledge with ethical discussions.
Analytics the Right Way: A Business Leader’s Guide to Putting Data to Productive Use
“Analytics the Right Way” offers a practical guide for business leaders to effectively leverage data analytics. The book presents a three-part framework that combines modern business realities with fundamental principles of statistics, computer science, and AI. It aims to transform overwhelming data into actionable insights, addressing the common “So what?” reaction to complex analytics. Using real-world examples and engaging illustrations, the authors provide a pragmatic approach to deliver clarity and business impact through data utilization. This guide empowers readers to apply analytical concepts effectively in a business context, turning data into a source of sustainable competitive advantage.
Hacks, Leaks, and Revelations: The Art of Analyzing Hacked and Leaked Data
“Hacks, Leaks, and Revelations” by Micah Lee is a comprehensive guide for journalists and researchers to uncover hidden truths in large datasets. The book combines practical techniques for data analysis with lessons on coding, security, and digital investigation. Lee provides real-world examples from various sources, including government agencies and extremist groups, to illustrate how to navigate and extract valuable information from leaked data. The guide covers essential skills such as keyword searching, Python programming for data analysis, secure communication with whistleblowers, and handling sensitive information. It equips readers with the tools to conduct impactful investigative journalism in the digital age.
Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future
“Predatory Data” by Anita Say Chan explores the connection between 19th-century eugenics and modern big data practices. The book highlights how historical anti-immigration and eugenics movements relate to current surveillance and algorithmic discrimination systems. Chan analyzes global patterns of dispossession and segregation perpetuated by dominant institutions in the data age. She also examines the history of resistance to these practices, showcasing how marginalized groups developed alternative data approaches that continue to influence justice-oriented data initiatives today. The book aims to foster a new historical perspective rooted in the pursuit of global justice.
Counting Feminicide: Data Feminism in Action
“Counting Feminicide” by Catherine D’Ignazio highlights the crucial work of data activists in Latin America who document feminicide, challenging mainstream data science practices. These activists meticulously collect and disseminate information on gender-related killings, emphasizing care, memory, and justice. Their efforts reveal the potential of restorative/transformative data science, aiming to heal communities and work towards eliminating gender-related violence. The book explores the power and limitations of quantification in addressing complex social issues, showcasing how data feminism in practice can contribute to a collective demand for rights restoration and gender order transformation.
Just Enough Data Science and Machine Learning: Essential Tools and Techniques
“Just Enough Data Science and Machine Learning” by Mark Levene and Martyn Harris offers an accessible introduction to data science and machine learning. The book covers fundamental statistical concepts, exploratory data analysis, hypothesis formation, and pattern discovery. It emphasizes practical applications with minimal math, using real-world datasets and Python code examples. Key topics include visualization tools, statistical modeling, machine learning methods, social network analysis, and sentiment analysis. The authors provide clear explanations of core concepts, making it an ideal resource for beginners seeking to develop intuition in data science without extensive mathematical background.
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.
Book summaries composed with AI-assistance.