This landmark textbook takes a whole subject approach to Information Science as a discipline. Introduced by leading international scholars and offering a global perspective on the discipline, this is designed to be the standard text for students worldwide. The authors' expert narrative guides you through each of the essential building blocks of information science offering a concise introduction and expertly chosen further reading and resources. Critical topics covered include: foundations: - concepts, theories and historical perspectives - organising and retrieving information - information behaviour, domain analysis and digital literacies - technologies, digital libraries and information management - information research methods and informetrics - changing contexts: information society, publishing, e-science and digital humanities - the future of the discipline. Readership: Students of information science, information and knowledge management, librarianship, archives and records management worldwide. Students of other information-related disciplines such as museum studies, publishing, and information systems and practitioners in all of these disciplines.

This book comprises an introduction to information as an external commodity; a data base that can be manipulated, retrieved, transmitted, and used. It is useful at an introductory undergraduate level and also for anyone who is new to the field of Information Science.

First Edition of this book is predominantly envisioned for students who want to redefine the way they think about artificial intelligence (AI) and Data Science. Therefore the book, which is organized as a assortment of essentially self-contained articles, comprises both general strategic considerations and some detailed sector-specific material. It shares visions into what it means to work with AI and how to do it more proficiently; how to use AI in detailed industries such as investment or insurance; how AI interrelates with other technologies such as blockchain. Rudra Tiwari

This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.

This book presents the basics of quantum information, e.g., foundation of quantum theory, quantum algorithms, quantum entanglement, quantum entropies, quantum coding, quantum error correction and quantum cryptography. The required knowledge is only elementary calculus and linear algebra. This way the book can be understood by undergraduate students. In order to study quantum information, one usually has to study the foundation of quantum theory. This book describes it from more an operational viewpoint which is suitable for quantum information while traditional textbooks of quantum theory lack this viewpoint. The current book bases on Shor's algorithm, Grover's algorithm, Deutsch-Jozsa's algorithm as basic algorithms. To treat several topics in quantum information, this book covers several kinds of information quantities in quantum systems including von Neumann entropy. The limits of several kinds of quantum information processing are given. As important quantum protocols, this book contains quantum teleportation, quantum dense coding, quantum data compression. In particular conversion theory of entanglement via local operation and classical communication are treated too. This theory provides the quantification of entanglement, which coincides with von Neumann entropy. The next part treats the quantum hypothesis testing. The decision problem of two candidates of the unknown state are given. The asymptotic performance of this problem is characterized by information quantities. Using this result, the optimal performance of classical information transmission via noisy quantum channel is derived. Quantum information transmission via noisy quantum channel by quantum error correction are discussed too. Based on this topic, the secure quantum communication is explained. In particular, the quantification of quantum security which has not been treated in existing book is explained. This book treats quantum cryptography from a more practical viewpoint.

Book Summary This book contain the following topics: • Introduction to Library and Information Science • Information Resource Management • Information Retrieval • Information Services and Programs • Collection Development and Management • Information Technology and Systems • Research Methods in Library and Information Science • Communication in Library and Information Science Manage your library and information science effectively. This essential book provides best practices for organizations to design recordkeeping systems, control records from creation to disposal, and preserve records of enduring value. Learn how to develop a records retention schedule, conduct a records inventory, and provide security and access controls. This book delivers a comprehensive overview of records and information management in the digital age

Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises.

The book systematically introduces the basic contents of data science, including data preprocessing and basic methods of data analysis, handling special problems (e.g. text analysis), deep learning, and distributed systems.In addition to systematically introducing the basic content of data science from a theoretical point of view, the book also provides a large number of data analysis practice cases.

No detailed description available for "Information Science in Theory and Practice".

An Introduction to Data Science with Python by Jeffrey S. Saltz and Jeffery M. Stanton provides readers who are new to Python and data science with a step-by-step walkthrough of the tools and techniques used to analyze data and generate predictive models. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using Python-based Jupyter Notebooks. The techniques include making tables and data frames, computing statistics, managing data, creating data visualizations, and building machine learning models. Each chapter breaks down the process into simple steps and components so students with no more than a high school algebra background will still find the concepts and code intelligible. Explanations are reinforced with linked practice questions throughout to check reader understanding. The book also covers advanced topics such as neural networks and deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence. With their trademark humor and clear explanations, Saltz and Stanton provide a gentle introduction to this powerful data science tool. Included with this title: LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don′t use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site.