Data Engineering for Data Science
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Beschreibung
This open access book aims to synthesize and integrate the research challenges in data science and data engineering. It offers a comprehensive survey of the entire data management stack, from scalable and explainable data analytics to traceable data workflows. By providing a consistent framework, it facilitates a thorough understanding of the data science lifecycle, from basic definitions to state-of-the-art concepts and techniques.
The book is divided into four parts, each focusing on a different aspect of the data management and science lifecycle: governance, storage and processing, preparation, and analysis. Each part is organized to provide a coherent conceptual framework and is divided into multiple chapters, each focusing on a specific topic but together offering a comprehensive overview of the state of the art and the key challenges in the respective areas. While the parts and chapters follow a logical sequence, each chapter is designed to be self-contained and can be read independently. Chapters include references for further reading and deeper exploration, and often also provide concrete examples or use cases to make the material more accessible. In addition, many chapters introduce a taxonomy to break down complex research areas into manageable components, highlighting the core directions and developments within each domain.
The book is designed to be a valuable resource for both researchers and practitioners seeking to leverage data engineering for data science applications. For both seasoned experts or budding professionals, it provides the tools and knowledge needed to stay at the forefront of data-driven advancements.
This open access book aims to synthesize and integrate the research challenges in data science and data engineering. It offers a comprehensive survey of the entire data management stack, from scalable and explainable data analytics to traceable data workflows. By providing a consistent framework, it facilitates a thorough understanding of the data science lifecycle, from basic definitions to state-of-the-art concepts and techniques.
The book is divided into four parts, each focusing on a different aspect of the data management and science lifecycle: governance, storage and processing, preparation, and analysis. Each part is organized to provide a coherent conceptual framework and is divided into multiple chapters, each focusing on a specific topic but together offering a comprehensive overview of the state of the art and the key challenges in the respective areas. While the parts and chapters follow a logical sequence, each chapter is designed to be self-contained and can be read independently. Chapters include references for further reading and deeper exploration, and often also provide concrete examples or use cases to make the material more accessible. In addition, many chapters introduce a taxonomy to break down complex research areas into manageable components, highlighting the core directions and developments within each domain.
The book is designed to be a valuable resource for both researchers and practitioners seeking to leverage data engineering for data science applications. For both seasoned experts or budding professionals, it provides the tools and knowledge needed to stay at the forefront of data-driven advancements.
This book is open access, which means that you have free and unlimited access Enables a deep understanding of the data science lifecycle, from basic definitions to advanced concepts and techniques Offers a comprehensive survey from scalable and explainable data analytics to traceable data workflows Covers all aspects of the data lifecycle: governance, storage and processing, preparation, and analysis
Autor*in
Gilles Dejaegere
Themen in »Data Engineering for Data Science«
Open Access data science data analysis information retrieval data governance information management machine learning time series analysis federated learning data lakes