Sustainable Statistical and Data Science Methods and Practices

ISBN 10: 3031413512 / ISBN 13: 9783031413513

Editore: Springer Nature Switzerland, 2024

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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Gathers papers from research conducted at LISA 2020, a global network of statistical laboratoriesShowcases the practical outcomes of using statistics and data science to address real-world problemsHighlights innovative initiatives for teach.

Codice articolo 927315802

Dati bibliografici

• Titolo: Sustainable Statistical and Data Science Methods and Practices
• Editore: Springer Nature Switzerland
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031413512
• ISBN 13: 9783031413513
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

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 Network aims 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.

Informazioni sull?autore

O. Olawale Awe, PhD is a distinguished academic and statistician. He currently serves as the Vice President of the International Association for Statistics Education (IASE). His impressive affiliations include being an elected 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 team leader at the Statistical Learning Laboratory (SaLLy) of the Federal University of Bahia, Brazil. 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. Awe's dedication to capacity-building is evident through the numerous workshops and seminars he has facilitated globally. His passion for enhancing the skills of statisticians in Africa and other developing countries drives his work. Holding a Ph.D. in Statistics from the University of Ibadan, Nigeria, and an MBA from Obafemi Awolowo University, Ile-Ife, Nigeria, Awe is an Affiliate Member of the prestigious African Academy of Sciences (AAS). His leadership extends to roles such as a former Council Member of the International Society for Business and Industrial Statistics (ISBIS) (2017-2021) and a country coordinator (Nigeria) for the International Statistical Literacy Program (ISLP) of the ISI. Currently, he serves as a professor and academic director of data science at Anointed University, South Africa, a professor and Ph.D. advisor at the Global Humanistic University (GHU), Curacao, and holds a visiting professorship at the African Institute of Mathematical Sciences (AIMS). Awe's multifaceted contributions underscore his commitment to advancing statistical education and research on a global scale.

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 Global Director of the LISA 2020 Network. He researches the micro- and macro-theory of collaboration, i.e., what individual statisticians and data scientists need to know to become effective interdisciplinary collaborators and what institutions can do to promote interdisciplinary collaboration between domain experts, statisticians, and data scientists. He is an Elected Member of the International Statistical Institute and a member of its Statistical Capacity Building Taskforce. He is a Fellow of the American Statistical Association (ASA) and winner of the 2019 ASA Jackie Dietz Award for best paper in the Journal of Statistics Education for “The ASCCR Frame for Learning Essential Collaboration Skills.” He is a past chair of the ASA Section on Statistical Consulting and the Conference on Statistical Practice. He has traveled through 85 countries and keeps these experiences in mind as he collaborates toward building capacity for sustainable development.