Data science in practice [electronic resource] / edited by Alan Said, Vicenc Torra.
- 其他作者：
- 其他題名：
- Studies in big data ;
- 出版： Cham : Springer International Publishing :Imprint: Springer 2019.
- 叢書名： Studies in big data,v.46
- 主題： Big data. , Machine learning. , Data mining. , Quantitative research. , Artificial intelligence--Data processing. , Business intelligence. , Computational Intelligence. , Big Data/Analytics. , Artificial Intelligence. , Data Mining and Knowledge Discovery.
- ISBN： 9783319975566 (electronic bk.) 、 9783319975559 (paper)
- URL：
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電子書(校內)
- 一般註：Artificial intelligence -- Machine learning: a concise overview -- Information fusion -- Information retrieval & recommender systems -- Business intelligence -- Data privacy -- Visual data analysis -- Complex data analysis -- Big data programming with Apache Spark. E1084學校採購電子書
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讀者標籤：
- 系統號： 000274227 | 機讀編目格式
館藏資訊
This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.
摘要註
This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.
