Sustainable Statistical and Data Science Methods and Practices : Reports from LISA 2020 Global Network, Ghana, 2022 / edited by O. Olawale Awe, Eric A. Vance.

Format:

Book

Contributor:

Awe, O. Olawale, editor.

Vance, Eric A., editor.

Series:

STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health, 2520-1948

Language:

English

Subjects (All):

Artificial intelligence--Data processing.

Artificial intelligence.

Data mining.

Machine learning.

Data Science.

Data Mining and Knowledge Discovery.

Statistical Learning.

Local Subjects:

Data Science.

Data Mining and Knowledge Discovery.

Statistical Learning.

Physical Description:

1 online resource (XXIV, 415 p. 150 illus., 127 illus. in color.)

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2023.

Summary:

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.

Contents:

Chapter. 1. Using social media and network services to promote statistical collaboration laboratories: A case study of LEA Brazil

Chapter. 2. Renewable Energy Forecasting Using Deep Learning Models

Chapter. 3. Exploring feature selection and supervised classification algorithms for predicting Obesity among rural women for policy decisions

Chapter. 4. Re-examining Inflation and its drivers in Nigeria: A machine learning approach

Chapter. 5. Estimating Relative Response Rates and Preferential Ranking of Subjects

Chapter. 6. Wealth Creation and Poverty Alleviation in a Nigerian State: A Recent Evidence-Based Survey

Chapter. 7. Effect of Statistics on Collaboration for Enhancing Institutional Sustainability: A Case of Mzumbe University-Tanzania

Chapter. 8. Strategies for the Sustainability of Stat Labs: A Case Study of Laboratory of Interdisciplinary Statistical Analysis, Lahore College for Women University Lahore, Pakistan (LISA-LCWU)

Chapter. 9. Advanced Mathematics and Computations for Innovation and Sustainability of Modern Statistics Laboratory

Chapter. 10. A New Estimator for the GPD Parameters under the POT Approach

Chapter. 11. A simple yet Robust Estimation of binned data: Egypt Income distribution and Geographical Inequality

Chapter. 12. Supervised Machine Learning Classification Algorithms: Some Applications and Code Snippets for Practical Implementations in Python Programming

Chapter. 13. Exploring the spatial variability and different determinants of co-existence of under-nutritional status among children in India through a Bayesian geo-additive multinomial regression model

Chapter. 14. Predicting the Nature of Terrorist Attacks in Nigeria Using Bayesian Neural Network Model

Chapter. 15. Salvage Value from Deterioration (SVD): An Optimal Inventory Model for Chicken Egg Marketing

Chapter. 16. Structural Equation Modeling with Stata: Illustration using a Population-Based, Nationally-Representative Dataset

Chapter. 17. Time series forecasting of seasonal non-stationary climate data: A comparative study

Chapter. 18. Weighted Hard and Soft Voting Ensemble Machine Learning CLASIFIERS: Application to Anaemia Diagnosis

Chapter 19. Machine Learning Approaches for Handling Imbalances in Health Data Classification

Chapter. 20. The Intersection of Data and Statistics with Sustainable Development Goals

Chapter. 21. Teaching Data Science in Africa via Online Team-Based Learning.

ISBN:

3-031-41352-0