Library Services, Resources, and News: Spring 2025
I'm Randy Souther, the library's liaison for Data Science.
This page highlights recent news, as well as some of the library's services and resources available to support faculty teaching and research. It will be updated ahead of each semester, and periodically in-between.
Selected Services and Resources
Reminders of key resources ahead of each semester—see more in Faculty Resources:
Class Instruction
I will meet with you and your class to provide an overview of library resources and research strategies tailored to specific assignments. (Please request at least two weeks in advance.)
Appointments
If you are new faculty and would like a personal overview of what the library offers—or if you've been around a while and would like a refresher—please schedule a time to meet.
Course Reserves
The library can place both physical and online materials on reserve for your classes. We encourage you to make arrangements prior to the start of each semester or as soon as possible, and email gleesonreserves@usfca.edu with any questions.
Items previously on reserve must be renewed every semester that you wish to have them availalbe.
Research Guides
We curate research guides for a variety of disciplines, topics, and classes. Please consider including a link to the Data Science guide on your course canvas pages for your students.
Book Orders
If you would like us to purchase specific books for the library, please contact me via email with the information: randall.souther@usfca.edu. We're happy to partner with you to build a rich and useful collection.
Streaming Videos for Classroom Use
If you’re planning on using streaming videos from the library, we encourage you to make arrangements prior to the start of each semester:
Even if you’ve made arrangements for a specific title in the past, please confirm that the license will be active during the semester. Many of our streaming videos are licensed for only a year at a time, and we want to make sure you and your students have access when you need it. License expiration dates, when applicable, are indicated in the “Access Restrictions” note in the catalog record for the video.
You can search our streaming video collection in the library’s catalog. For additional information, please see our Video and Streaming Media for Faculty guide, or contact me for help.
Open Access Publishing
Learn about open-access opportunities for your work.
Transformative Agreements
See our current agreements with journal publishers to allow USF faculty to publish their articles open access at reduced or no cost to the author.
Scholarship Repository
We encourage you to add your publications to the library's repository of USF scholarship for open access and archiving.
News
April 2025
New Library Search Interfaces.
Gleeson Library will launch new, more intuitive interfaces for Fusion and EBSCOhost databases on June 16. The update offers improved design, accessibility, and features.
Users must manually migrate any saved alerts and folders. See the Fusion and EBSCOhost Database Updates guide for more information, FAQs, and a link to preview the new interface.
In addition to Fusion, 77 EBSCOhost databases will implement the new interface, including:
- Business Source Complete
- Inspec
Discarding VHS Collection
Gleeson Library is planning to deaccession our VHS collection of 1,155 items. We are no longer able to support this obsolete format. None of the tapes has circulated in at least two years, some for much longer or not at all.
Please see the list of VHS holdings under consideration for discard. If there are any you feel are essential to retain, please inform your library liaison Randy Souther by May 15.
We are working with the office of Environmental Sustainability to recycle the tapes.
February 2025
New transformative agreement with Oxford University Press, and extended agreements with Cambridge University Press and Springer.
Transformative agreements allow you to publish your articles open access with no fees or reduced fees. Our current agreements are with ACM: Association for Computing Machinery, Oxford, Cambridge, Elsevier, Springer, and American Chemical Society.
View our Transformative Agreements
January 2025
ProQuest Ebook Central subscription ends February 1, 2025
ProQuest Ebook Central is one of our major ebook providers, and its cancellation will affect more than 250,000 titles in our collection.
If you rely on any specific ebook title for assigned readings in your courses, or for your own teaching or research, please check the ebook status below, and notify me promptly ( randall.souther@usfca.edu ) if the ebook is from ProQuest Ebook Central. Gleeson Library may be able to provide alternate access to frequently used ebooks.
See the link below to check the status of any ebook in question.
See the News Archive to view past news items for this and selected other disciplines.
Selected New Books in Data Science
Book summaries composed with AI-assistance.
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.
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.
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.
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.
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.
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.
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.
Beautiful Math: The Surprisingly Simple Ideas Behind the Digital Revolution in How We Live, Work, and Communicate
“Beautiful Math” by Chris Bernhardt explores the mathematical foundations of the digital age. The book covers four main themes: information, communication, computation, and learning. Bernhardt uses simple mathematical models to reveal deep connections between seemingly unrelated concepts, explaining key ideas like information theory, digital-analog conversion, algorithms, and neural networks. The author aims to present these complex topics with minimal mathematics, making them accessible to a wide audience. Historical anecdotes provide context for technological developments. The book offers readers, regardless of their mathematical background, an engaging journey through the mathematical principles underlying our digital world.
Cyberboss: The Rise of Algorithmic Management and the New Struggle for Control at Work
How technologies of organization are redrawing the lines of class struggle. Across the world, algorithms are changing the nature of work. Nowhere is this clearer than in the logistics and distribution sectors, where workers are instructed, tracked and monitored by increasingly dystopian management technologies. In Cyberboss, Craig Gent takes us into workplaces where algorithms rule to excavate the politics behind the newest form of managerial power. Combining worker testimony and original research on companies such as Amazon, Uber, and Deliveroo, the cutting edge of algorithmic management technology, this book reveals the sometimes unexpected effects these new techniques have on work, workers and managers. Gent advances an alternative politics of resistance in the face of digital control.
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.
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.