I. General perspectives on big data -- II. Data-centric, exploratory methods -- III. Efficient algorithms -- IV. Graph approaches -- V. Model fitting and regularization -- VI. Ensemble methods -- VII. Causal inference -- VIII. Targeted learning
Summary
"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"-- Provided by publisher
Notes
"Chapman & Hall book."
Bibliography
Includes bibliographical references
Notes
Online resource; title from PDF title page (EBSCO, viewed February 19, 2016)
