Know-how for Data Scientists

A clear and application-oriented introduction with numerous use cases and practical examples from various industries. Potential benefits as well as possible pitfalls are highlighted. Currently, Data Science is unparalleled in its representation of analyzing large data sets using analytical concepts from Machine Learning or Artificial Intelligence. Following the conscious recognition of Big Data, especially its availability in companies, technologies and methods for analysis are required where traditional Business Intelligence reaches its limits.

This book provides a comprehensive introduction to Data Science and its practical relevance for businesses. It also addresses the integration of Data Science into an existing Business Intelligence ecosystem. Various contributions explain both fields of activity and methods as well as role and organizational models that interact with concepts and architectures related to Data Science.

This second revised edition has been expanded to include new topics such as Feature Selection and Deep Reinforcement Learning, as well as a new case study.