Data Science and Cases in Sustainability
Data Mining and Big Data Analysis
Buch in deiner Nähe kaufen
Beschreibung
This book discusses the fascinating world of data science and cases in sustainability focusing on the properties of two modern data science fields—data mining and big data analytics—along with their interdependencies, emphasizing sustainable applications that directly address topics related to SDG 9 (Industry, Innovation and Infrastructure). It explains the practical understanding of all the techniques and tools of data mining and big data analytics, along with their mathematical foundations and sustainable applications. Each of these two techniques plays an important part in revealing buried information from massive volume of data. The book emphasizes the interdisciplinary character of data science, relying on topics such as computer science, statistics, physics, economics and more to provide readers with a comprehensive understanding. It carefully takes the readers through the data science pipeline, including data collection, preparation, analysis, management, visualization and storage of high volumes of stationary and non-stationary data and its sustainable applications.
The second of the two volumes, the book digs into the methodologies and algorithms utilized at each level, including key learning mechanisms, association analysis, classification, clustering and outlier analysis. Furthermore, it provides insights on the growing discipline of deep learning and the utilization of distributed systems to handle large amounts of data. Case studies demonstrate the sustainable uses of data in a variety of fields from a big data perspective. Emphasizes the need of data science in handling this “big data” flood, it dives into the technological complexities of massive data storage and processing, emphasizing advances in artificial intelligence that enable successful analysis. Data mining and big data analysis approaches are thoroughly examined in the book, emphasizing their interplay and unique contributions to unravelling the secrets concealed inside massive data sets. The book provides a clear roadmap for navigating the complex world of big data analysis, providing readers with the information and tools they need to gain useful insights from the ever-increasing types of data and, ultimately, to shape the future through data-driven real time decisions.
This book discusses the fascinating world of data science and cases in sustainability focusing on the properties of two modern data science fields—data mining and big data analytics—along with their interdependencies, emphasizing sustainable applications that directly address topics related to SDG 9 (Industry, Innovation and Infrastructure). It explains the practical understanding of all the techniques and tools of data mining and big data analytics, along with their mathematical foundations and sustainable applications. Each of these two techniques plays an important part in revealing buried information from massive volume of data. The book emphasizes the interdisciplinary character of data science, relying on topics such as computer science, statistics, physics, economics and more to provide readers with a comprehensive understanding. It carefully takes the readers through the data science pipeline, including data collection, preparation, analysis, management, visualization and storage of high volumes of stationary and non-stationary data and its sustainable applications.
The second of the two volumes, the book digs into the methodologies and algorithms utilized at each level, including key learning mechanisms, association analysis, classification, clustering and outlier analysis. Furthermore, it provides insights on the growing discipline of deep learning and the utilization of distributed systems to handle large amounts of data. Case studies demonstrate the sustainable uses of data in a variety of fields from a big data perspective. Emphasizes the need of data science in handling this “big data” flood, it dives into the technological complexities of massive data storage and processing, emphasizing advances in artificial intelligence that enable successful analysis. Data mining and big data analysis approaches are thoroughly examined in the book, emphasizing their interplay and unique contributions to unravelling the secrets concealed inside massive data sets. The book provides a clear roadmap for navigating the complex world of big data analysis, providing readers with the information and tools they need to gain useful insights from the ever-increasing types of data and, ultimately, to shape the future through data-driven real time decisions.
Discusses algorithms for data mining and analysis with deeper theoretical foundations and big data handling Helps understand the underlying algorithms and tools for data mining and big data analytics Includes case studies of real time decision-making from stationary, non-stationary and distributed data
Autor*in
Ashish Ghosh
Themen in »Data Science and Cases in Sustainability«
data mining data storage association and correlation frequent pattern generation frequent itemset generation incremental rule mining association mining correlation analysis classification prediction cluster analysis anomaly detection distributed data mining advantages and disadvantages of data mining big data analysis