Awe olawale (15 Ergebnisse)

Zustand: New. In.

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Taschenbuch. Zustand: Neu. Neuware.

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Paperback. Zustand: Brand New. 634 pages. 10.00x7.00x10.00 inches. In Stock.

Zustand: New. In.

Zustand: New.

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume gathers papers presented at the LISA 2020 Sustainability Symposium in Kumasi, Ghana, May 2-6, 2022. They focus on sustainable methods and practices of using statistics and data science to address real-world problems. From utilizing social media for statistical collaboration to predicting obesity among rural women, and from analyzing inflation in Nigeria using machine learning to teaching data science in Africa, this book explores the intersection of data, statistics, and sustainability. With practical applications, code snippets, and case studies, this book offers valuable insights for researchers, policymakers, and data enthusiasts alike.The LISA 2020 Global Networkaims to enhance statistical and data science capability in developing countries through the creation of a network of collaboration laboratories (also known as'stat labs'). These stat labs are intended to serve as engines for development by training the next generation of collaborative statisticians and data scientists, providing research infrastructure for researchers, data producers, and decision-makers, and enabling evidence-based decision-making that has a positive impact on society. The research conducted at LISA 2020 focuses on practical methods and applications for sustainable growth of statistical capacity in developing nations.…

Taschenbuch. Zustand: Neu. Sustainable Statistical and Data Science Methods and Practices | Reports from LISA 2020 Global Network, Ghana, 2022 | O. Olawale Awe (u. a.) | Taschenbuch | STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health | xxiv | Englisch | 2025 | Springer | EAN 9783031413544 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Econometric Time Series Analysis | A quantitative focus on a developing economy | Olushina Olawale Awe | Taschenbuch | 72 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659212765 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Practical Statistical Learning and Data Science Methods | Case Studies from LISA 2020 Global Network, USA | O. Olawale Awe (u. a.) | Taschenbuch | STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health | xxix | Englisch | 2025 | Springer | EAN 9783031722172 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Hardcover. Zustand: Brand New. 576 pages. 10.00x7.00x1.46 inches. In Stock.

Hardcover. Zustand: Brand New. 415 pages. 9.50x6.25x1.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science.Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike.The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as 'stat labs.' These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers.…