Foundations of predictive analytics, Wu, James Coggeshall, Stephen
Автор: Valentine Fontama; Roger Barga; Wee Hyong Tok Название: Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition ISBN: 1484212010 ISBN-13(EAN): 9781484212011 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models.
Описание: Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery.
Автор: Larose Daniel T Название: Data Mining and Predictive Analytics ISBN: 1118116194 ISBN-13(EAN): 9781118116197 Издательство: Wiley Рейтинг: Цена: 18842.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis.
Автор: Chorianopoulos Antonios Название: Effective CRM Using Predictive Analytics ISBN: 1119011558 ISBN-13(EAN): 9781119011552 Издательство: Wiley Рейтинг: Цена: 7278.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts.
Описание: This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling.
Автор: Winters Ralph Название: Practical Predictive Analytics ISBN: 1785886185 ISBN-13(EAN): 9781785886188 Издательство: Неизвестно Рейтинг: Цена: 10114.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Ivo D. Dinov Название: Data Science and Predictive Analytics ISBN: 3030101878 ISBN-13(EAN): 9783030101879 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Поставка под заказ.
Описание: Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. The two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). This can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook. • A geriatric neurologist is examining a patient complaining of gait imbalance and posture instability. To determine if the patient may suffer from Parkinson’s disease, the physician acquires clinical, cognitive, phenotypic, imaging, and genetics data (Big Data). Most clinics and healthcare centers are not equipped with skilled data analytic teams that can wrangle, harmonize and interpret such complex datasets. A learner that completes a course of study using this textbook will have the competency and ability to manage the data, generate a protocol for deriving biomarkers, and provide an actionable decision support system. The results of this protocol will help the physician understand the entire patient dataset and assist in making a holistic evidence-based, data-driven, clinical diagnosis.
• To improve the return on investment for their shareholders, a healthcare manufacturer needs to forecast the demand for their product subject to environmental, demographic, economic, and bio-social sentiment data (Big Data). The organization’s data-analytics team is tasked with developing a protocol that identifies, aggregates, h
Описание: This comprehensive new resource focuses on applied electromagnetics and takes readers beyond the conventional theory with the use of contemporary mathematics to improve the practical use of electromagnetics in emerging areas of field communications, wireless power transfer, metamaterials, MIMO and direction-of-arrival systems. The book explores the existing and novel theories and principles of electromagnetics in order to help engineers analyze and design devices for today’s applications in wireless power transfers, NFC, and metamaterials.
Описание: Data science has a huge impact on how companies conduct business, and those who don`t learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers.
Описание: Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-Making Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen provides a holistic approach covering key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies--including lessons from failed projects. It is all designed to help you gain a practical understanding you can apply for profit. * Leverage knowledge extracted via data mining to make smarter decisions * Use standardized processes and workflows to make more trustworthy predictions * Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting) * Understand predictive algorithms drawn from traditional statistics and advanced machine learning * Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection
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