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Full Description
Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice.
Offering balanced coverage of methodology, theory, and applications, this handbook:
Describes modern, scalable approaches for analyzing increasingly large datasets
Defines the underlying concepts of the available analytical tools and techniques
Details intercommunity advances in computational statistics and machine learning
Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.
Contents
General Perspectives on Big Data. Data-Centric, Exploratory Methods. Efficient Algorithms. Graph Approaches. Model Fitting and Regularization. Ensemble Methods. Causal Inference. Targeted Learning.
