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Author: Yunfei Du
Publisher: Libraries Unlimited
ISBN: 1440871213
Category : Computers
Languages : en
Pages : 0
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Book Description
More data, more problems -- A new strand of librarianship -- Data creation and collection -- Data for the academic librarian -- Research data services and the library ecosystem -- Data sources -- Data curation (archiving/preservation) -- Data storage, management, and retrieval -- Data analysis and visualization -- Data ethics and policies -- Data for public libraries and special libraries -- Conclusion: library, information, and data science.
Author: Yunfei Du
Publisher: Bloomsbury Publishing USA
ISBN: 1440871221
Category : Language Arts & Disciplines
Languages : en
Pages : 181
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Book Description
This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries. Data Science for Librarians introduces data science to students and practitioners in library services. Writing for academic, public, and school library managers; library science students; and library and information science educators, authors Yunfei Du and Hammad Rauf Khan provide a thorough overview of conceptual and practical tools for data librarian practice. Partially due to how quickly data science evolves, libraries have yet to recognize core competencies and skills required to perform the job duties of a data librarian. As society transitions from the information age into the era of big data, librarians and information professionals require new knowledge and skills to stay current and take on new job roles, such as data librarianship. Such skills as data curation, research data management, statistical analysis, business analytics, visualization, smart city data, and learning analytics are relevant in library services today and will become increasingly so in the near future. This text serves as a tool for library and information science students and educators working on data science curriculum design.
Author: Yunfei Du
Publisher: Libraries Unlimited
ISBN: 1440871213
Category : Computers
Languages : en
Pages : 0
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Book Description
More data, more problems -- A new strand of librarianship -- Data creation and collection -- Data for the academic librarian -- Research data services and the library ecosystem -- Data sources -- Data curation (archiving/preservation) -- Data storage, management, and retrieval -- Data analysis and visualization -- Data ethics and policies -- Data for public libraries and special libraries -- Conclusion: library, information, and data science.
Author: Mani, Nandita S.
Publisher: IGI Global
ISBN: 1799897044
Category : Language Arts & Disciplines
Languages : en
Pages : 415
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Book Description
Beyond providing space for data science activities, academic libraries are often overlooked in the data science landscape that is emerging at academic research institutions. Although some academic libraries are collaborating in specific ways in a small subset of institutions, there is much untapped potential for developing partnerships. As library and information science roles continue to evolve to be more data-centric and interdisciplinary, and as research using a variety of data types continues to proliferate, it is imperative to further explore the dynamics between libraries and the data science ecosystems in which they are a part. The Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems provides a global perspective on current and future trends concerning the integration of data science in libraries. It provides both a foundational base of knowledge around data science and explores numerous ways academicians can reskill their staff, engage in the research enterprise, contribute to curriculum development, and help build a stronger ecosystem where libraries are part of data science. Covering topics such as data science initiatives, digital humanities, and student engagement, this book is an indispensable resource for librarians, information professionals, academic institutions, researchers, academic libraries, and academicians.
Author: Yuxing Yan
Publisher: Packt Publishing Ltd
ISBN: 1788834739
Category : Computers
Languages : en
Pages : 356
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Book Description
Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda Key Features -Use Anaconda to find solutions for clustering, classification, and linear regression -Analyze your data efficiently with the most powerful data science stack -Use the Anaconda cloud to store, share, and discover projects and libraries Book Description Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R. What you will learn Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda Use the package manager conda and discover, install, and use functionally efficient and scalable packages Get comfortable with heterogeneous data exploration using multiple languages within a project Perform distributed computing and use Anaconda Accelerate to optimize computational powers Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud Tackle advanced data prediction problems Who this book is for Hands-On Data Science with Anaconda is for you if you are a developer who is looking for the best tools in the market to perform data science. It’s also ideal for data analysts and data science professionals who want to improve the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected.
Author: Laura Igual
Publisher: Springer Nature
ISBN: 303148956X
Category :
Languages : en
Pages : 255
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Book Description
Author: Lillian Pierson
Publisher: John Wiley & Sons
ISBN: 1118841522
Category : Computers
Languages : en
Pages : 408
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Book Description
Discover how data science can help you gain in-depth insight into your business – the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer covering all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad aspects of the topic, including the sometimes intimidating field of big data and data science, it is not an instructional manual for hands-on implementation. Here’s what to expect in Data Science for Dummies: Provides a background in big data and data engineering before moving on to data science and how it’s applied to generate value. Includes coverage of big data frameworks and applications like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL. Explains machine learning and many of its algorithms, as well as artificial intelligence and the evolution of the Internet of Things. Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate. It’s a big, big data world out there – let Data Science For Dummies help you get started harnessing its power so you can gain a competitive edge for your organization.
Author: Tom Diamond
Publisher: McFarland
ISBN: 1476639981
Category : Language Arts & Disciplines
Languages : en
Pages : 199
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Book Description
As new technology and opportunities emerge through the revolutionary impacts of the digital age, the function of libraries and librarians and how they provide services to constituents is rapidly changing. The impact of new technology touches everything from libraries' organizational structures, business models, and workflow processes, to position descriptions and the creation of new positions. As libraries are required to make operational adjustments to meet the growing technological demands of libraries' customer bases and provide these services, librarians must be flexible in adapting to this fast-moving environment. This volume shares the unique perspectives and experiences of librarians on the front lines of this technological transformation. The essays within provide details of both the practical applications of surviving, adapting, and growing when confronted with changing roles and responsibilities, as well as a big picture perspective of the changing roles impacting libraries and librarians. This book strives to be a valuable tool for librarians involved in public and technical services, digital humanities, virtual and augmented reality, government documents, information technology, and scholarly communication.
Author: Mark Needham
Publisher: O'Reilly Media
ISBN: 1492047651
Category : Computers
Languages : en
Pages : 268
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Book Description
Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark
Author: Greg Foss
Publisher: "O'Reilly Media, Inc."
ISBN: 1492046450
Category : Computers
Languages : en
Pages : 329
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Book Description
Learn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data. Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths. You'll explore: Examples of how data analysis can help you solve several SAP challenges Natural language processing for unlocking the secrets in text Data science techniques for data clustering and segmentation Methods for detecting anomalies in your SAP data Data visualization techniques for making your data come to life
Author: Lisa Federer
Publisher: Rowman & Littlefield
ISBN: 1442264284
Category : Language Arts & Disciplines
Languages : en
Pages : 243
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Book Description
Technological advances and the rise of collaborative, interdisciplinary approaches have changed the practice of research. The 21st century researcher not only faces the challenge of managing increasingly complex datasets, but also new data sharing requirements from funders and journals. Success in today’s research enterprise requires an understanding of how to work effectively with data, yet most researchers have never had any formal training in data management. Libraries have begun developing services and programs to help researchers meet the demands of the data-driven research enterprise, giving librarians exciting new opportunities to use their expertise and skills. The Medical Library Association Guide to Data Management for Librarians highlights the many ways that librarians are addressing researchers’ changing needs at a variety of institutions, including academic, hospital, and government libraries. Each chapter ends with “pearls of wisdom,” a bulleted list of 5-10 takeaway messages from the chapter that will help readers quickly put the ideas from the chapter into practice. From theoretical foundations to practical applications, this book provides a background for librarians who are new to data management as well as new ideas and approaches for experienced data librarians.
