Data Science for Sustainable Development Goals Indian Case Studies | 41.47 MB
Title: Data Science for Sustainable Development Goals; Indian Case Studies
Author: Avik Sarkar
Category: Nonfiction, Computers, Database Management, Social & Cultural Studies, Political Science, Government, Public Policy, General Computing
Language: English | 238 Pages | ISBN: 9781040639559
Description:
This book presents real-life applications of data science, artificial intelligence, and big data for the Sustainable Development Goals. It includes a list of case studies from different states across India. The case studies in the book use both structured and unstructured data like numeric data, textual data, and video/image data across the various chapters for analysis. It explores various aspects of data science starting from on ground data collection to dashboard based reporting, unsupervised methods like clustering for grouping data points, use of artificial intelligence, machine learning and deep learning, search or information retrieval, time series forecasting, and optimization.
• It showcases data science decision-making processes, driving innovation, and solving complex problems in real-life scenarios across sectors like governance, education, healthcare agriculture and sanitation.
• The SDGs provide a framework for societal development and well-being for all; the data science and big data interventions in this book are aligned towards mapping the various SDGs.
• Most of the data science use cases and initiative projects covered in this book have been implemented by central or state governments across different states of India.
• Shows how data science intervention can transform the social sector, potentially driving positive change and addressing critical societal challenges.
• Explained the fundamentals of data science theories with case studies, including concepts like classification, regression, predictive analytics, optimization, artificial intelligence, deep learning, and time series forecasting for readers of different disciplines.
It serves as a valuable reference for graduate students, researchers, and scholars seeking to deepen their knowledge and engage with real life applications of data science. It will also serve as a valuable resource for government officers and policy practitioners, providing a range of cases on the use of data-based methods for improving governance and policy making.
The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license funded by by the Great Lakes Institute of Management, Gurgaon Campus, Delhi NCR, India.
This book presents real-life applications of data science, artificial intelligence, and big data for the Sustainable Development Goals. It includes a list of case studies from different states across India. The case studies in the book use both structured and unstructured data like numeric data, textual data, and video/image data across the various chapters for analysis. It explores various aspects of data science starting from on ground data collection to dashboard based reporting, unsupervised methods like clustering for grouping data points, use of artificial intelligence, machine learning and deep learning, search or information retrieval, time series forecasting, and optimization.
• It showcases data science decision-making processes, driving innovation, and solving complex problems in real-life scenarios across sectors like governance, education, healthcare agriculture and sanitation.
• The SDGs provide a framework for societal development and well-being for all; the data science and big data interventions in this book are aligned towards mapping the various SDGs.
• Most of the data science use cases and initiative projects covered in this book have been implemented by central or state governments across different states of India.
• Shows how data science intervention can transform the social sector, potentially driving positive change and addressing critical societal challenges.
• Explained the fundamentals of data science theories with case studies, including concepts like classification, regression, predictive analytics, optimization, artificial intelligence, deep learning, and time series forecasting for readers of different disciplines.
It serves as a valuable reference for graduate students, researchers, and scholars seeking to deepen their knowledge and engage with real life applications of data science. It will also serve as a valuable resource for government officers and policy practitioners, providing a range of cases on the use of data-based methods for improving governance and policy making.
The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license funded by by the Great Lakes Institute of Management, Gurgaon Campus, Delhi NCR, India.
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