Practical Statistical Learning and Data Science Methods: Case Studies from LISA 2020 Global Network, USA (STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health)

ISBN 10: 3031722140 / ISBN 13: 9783031722141

Editore: Springer, 2024

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Dati bibliografici

• Titolo: Practical Statistical Learning and Data Science Methods: Case Studies from LISA 2020 Global Network, USA (STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health)
• Editore: Springer
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031722140
• ISBN 13: 9783031722141
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

O. Olawale Awe holds a PhD in Statistics from the University of Ibadan, Nigeria, and an MBA from Obafemi Awolowo University, Ile-Ife, Nigeria. He currently serves as the Vice President of the International Association for Statistics Education (IASE). His affiliations include being an Elected Council Member of the International Statistics Institute (ISI), Vice President of Global Statistical Engagements of the LISA 2020 Global Network, USA, and a research professor and machine learning team leader at the Statistical Learning Laboratory (SaLLy) of the Federal University of Bahia, Brazil. He has published more than 100 research papers in international and national journals and conferences, and he has also published five books and monographs. As the pioneering LISA Fellow of the LISA 2020 Global Network at the University of Colorado, Boulder, USA, he has significantly contributed to the global statistical community.

Eric A. Vance is an Associate Professor of Applied Mathematics and the Director of the Laboratory for Interdisciplinary Statistical Analysis (LISA) at the University of Colorado Boulder, USA. He is the Director of the LISA 2020 Global Network. He is an Elected Member of the ISI and a Fellow of the American Statistical Association (ASA). Dr. Vance researches what individual statisticians and data scientists need to know to become effective interdisciplinary collaborators and what institutions can do to promote interdisciplinary collaboration to make data-driven decisions. He was the 2023 winner of the ASA’s W.J. Dixon Award for Excellence in Statistical Consulting.