Concise Survey of Computer Methods PDF Download

Author: Peter Naur
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 406

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Author: Andrew W. Phillips
Publisher: Elsevier Health Sciences
ISBN: 0323695922
Category : Medical
Languages : en
Pages : 138

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Book Description
Offering a practical, six-step approach to effective survey design, delivery, and analysis, Survey Methods for Medical and Health Professions Education provides a real-world framework for successful research and evaluation using surveys. Focused on medical and other health professional education research, this unique text features quick-reference checklists, high-yield explanations, and case examples throughout, making it both a foundational reference and a go-to resource for all health professions educators and researchers who use surveys. - Provides a detailed and practical discussion of the steps in survey methodology, along with just enough theory to inform common decisions. - Includes numerous real-world examples, both simple and complex, covering the wide range of problems researchers are likely to face. - Addresses issues of survey fatigue and addresses the challenge of how to get "good data." - Features "Voice of Experience" boxes—pearls based on authors' actual experiences. - Concludes each chapter with a checklist so readers can quickly ensure they have covered every necessary step of survey design and implementation. - Contains a convenient glossary of terms. - Offers guidance from expert international contributors in medical and health professions research. - Summarizes the most current standards and understanding of survey research so that experienced and novice researchers alike can directly apply content to improve research rigor.

Author:
Publisher:
ISBN:
Category : Standardization
Languages : en
Pages : 120

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Author: United States. National Bureau of Standards
Publisher:
ISBN:
Category : Industrial safety
Languages : en
Pages : 112

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Author: Alfred Z. Spector
Publisher: Cambridge University Press
ISBN: 1009272195
Category : Computers
Languages : en
Pages : 333

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Book Description
Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.

Author: Archana Patel
Publisher: CRC Press
ISBN: 1000881202
Category : Computers
Languages : en
Pages : 315

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Book Description
As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This first volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic technologies. Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as: Can semantic technologies facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for data science? This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and, thus, it is a unique resource for scholars, researchers, professionals, and practitioners in this field.

Author: Bouarara, Hadj Ahmed
Publisher: IGI Global
ISBN: 1799827933
Category : Computers
Languages : en
Pages : 351

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Book Description
Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

Author: Harry Yang
Publisher: CRC Press
ISBN: 100065267X
Category : Business & Economics
Languages : en
Pages : 335

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Book Description
The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise

Author: Jay Liebowitz
Publisher: CRC Press
ISBN: 1000094650
Category : Computers
Languages : en
Pages : 242

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Book Description
Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.

Author: Naresh K. Malhotra
Publisher: Emerald Group Publishing
ISBN: 1781907617
Category : Business & Economics
Languages : en
Pages : 308

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Book Description
Review of Marketing Research