Data

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Author : Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma
Genre : Language Arts & Disciplines

Summary : Learn how to process and analysis data using Python Key Features a- The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Authors Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of 'Social Network Analysis and Mining'. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development.

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Author : Piethein Strengholt
Genre : Computers

Summary : As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

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Author : Maria Cristina Paganoni
Genre : Language Arts & Disciplines

Summary : This book addresses big data as a socio-technical construct with huge potential for innovation in key sectors such as healthcare, government and business. Big data and its increasingly widespread use in such influential spheres can generate ethically controversial decisions, including questions surrounding privacy, consent and accountability. This book attempts to unpack the epistemological implications of the term ‘big data’, as well as the opportunities and responsibilities which come with it. The author analyses the linguistic texture of the big data narrative in the news media, in healthcare and in EU law on data protection, in order to contribute to its understanding from the critical perspective of language studies. The result is a study which will be of interest to students and scholars working in the digital humanities, corpus linguistics, and discourse studies.

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Author : OECD
Genre :

Summary : This report examines the opportunities of enhancing access to and sharing of data (EASD) in the context of the growing importance of artificial intelligence and the Internet of Things. It discusses how EASD can maximise the social and economic value of data re-use and how the related risks and challenges can be addressed. It highlights the trade-offs, complementarities and possible unintended consequences of policy action – and inaction. It also provides examples of EASD approaches and policy initiatives in OECD countries and partner economies.

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Author : Peter Folmer Hansen
Genre : Computers

Summary : In today’s competitive business landscape, the key to success lies within the heart of your organization’s data–the Master Data. This book brings together insights and techniques needed to navigate the complex world of Master Data governance and management, and revolutionize how Master Data and information are handled within an organization. Master Data, the backbone of every successful enterprise, encapsulates the fundamental information essential for conducting business seamlessly. At the same time, Master Data governance helps businesses stay focused on what really matters–using Master Data to grow revenue, cut hidden costs, and stay safe and compliant with regulations. This comprehensive guide dives into practical applications, showing you how to harness the potential of your Master Data resources to fuel strategic decision making, reliable business operations, and market-driven product innovation. As companies worldwide recognize the pivotal role of Master Data in shaping their future, the demand for comprehensive, actionable guidance is greater than ever. With a wealth of non-technical illustrations, real-world examples, and practical tools, this book demystifies the complexities of Master Data governance and management. At its core, this book introduces a proven Master Data governance framework that empowers you to take charge systematically and methodically. Whether you’re taking your first steps or you’re well-versed in Master Data governance and management, you’ll find step-by-step guidance to ensure that you’re always moving forward. What You Will Learn Easily distinguish your company’s data types within its data ecosystem using icons and illustrations Control Master Data using a unique Master Data governance framework with three interconnected gears: Master Data, systems, and organization Ensure that business data needs are met using a non-technical data modelling working tool that includes the core identifiers and attributes to describe customers, products, suppliers, and more Create the Master Data overview using illustrated data and system landscapes, and the data flow mapping method Create a Master Data organization using a described and illustrated structure, roles, and meeting forums Design Master Data processes, procedures, and policies using concrete Master Data quality examples Ensure high Master Data quality and data-driven decisions to realize data value Select and use Master Data platforms and use AI and machine learning wisely to achieve a single source of truth Improve the Master Data area in system implementation using a project framework, processes, and tools Who This Books Is For Data professionals without a theoretical data background and who work with data daily and need a deper understandng of what data is and how to handle master data and its quality in practice; trained data professionals needing inspiration as they seek to refine their methods and strategies for managing master data tasks and initiatives in practice; and readers who want more than a theory book and need a reference book with non-technical illustrations, practical work tools and methods, and concrete examples, plus instructions that can be used directly in daily work

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Author : United States. Weather Bureau
Genre :

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Author : Gokce Banu Laleci Erturkmen
Genre : Medical

Summary : Technology is a key driver for innovation in the medical and the health sectors at large. Data-intensive applications and services can provide better and more cost-efficient solutions with high impact for improved point-of-care solutions, integrating health data from different sources, tailored to the specific health care needs of the individual, thus helping to achieve better patient management and improved clinical outcomes. In Europe, the relatively recent proposal for a regulation to set up the European Health Data Space - aiming to unleash the full potential of health data - will open new opportunities for synergies and value co-creation as part of high-performance stakeholder-driven ecosystems, while also ensuring citizens to take control of their own health data. With regard to this special Research Topic, we consider health data spaces as cross-institutional organizational and technical solutions for safe and secure health data exchange between different stakeholders for better health care and research.