Website Search
Find information on spaces, staff, and services.
Catalog Search
Books
Handbook of big data
- Available as
- Online
- 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...
"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"--
Details
- Creator
- edited by Peter Buhlmann, Petros Drineas, Michael Kane, Mark van der Laan
- Format
- Books
- Language
- English
- Contributors
- Publication
-
- Boca Raton, FL : CRC Press, [2016]
- ©2016
- Physical Details
-
- 1 online resource (470 p.)
- ISBNs
- 9780429162985, 0429162987, 9781482249088, 1482249081, 1482249073
- OCLC
- ocn939597365
- A Chapman and Hall book.
- English
- Front Cover; Contents; Preface; Editors; Contributors; I. General Perspectives on Big Data; 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data; 2. Big-n versus Big-p in Big Data; II. Data-Centric, Exploratory Methods; 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data; 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems; 5. Interactive Visual Analysis of Big Data; 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator
- III. Efficient Algorithms7. High-Dimensional Computational Geometry; 8. IRLBA: Fast Partial Singular Value Decomposition Method; 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms; 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms; IV. Graph Approaches; 11. Networks; 12. Mining Large Graphs; V. Model Fitting and Regularization; 13. Estimator and Model Selection Using Cross-Validation; 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets; 15. Learning Structured Distributions
- 16. Penalized Estimation in Complex Models17. High-Dimensional Regression and Inference; VI. Ensemble Methods; 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation; 19. Scalable Super Learning; VII. Causal Inference; 20. Tutorial for Causal Inference; 21. A Review of Some Recent Advances in Causal Inference; VIII. Targeted Learning; 22. Targeted Learning for Variable Importance; 23. Online Estimation of the Average Treatment Effect; 24. Mining with Inference: Data-Adaptive Target Parameters; Back Cover