Data science in practice
Author(s)
Bibliographic Information
Data science in practice
（Studies in big data, v. 46）
Springer, c2019
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"
