Statistical Data Science (gnv64)

Statistical Data Science
By Niall Adams and Edward Cohen
Wspc (Europe) | June 2018 | ISBN-10: 1786345390 | PDF | 192 Pages | 15.7 mb
https://www.amazon.com/Statistical-Data-Science-Adams-Niall/dp/1786345390

As an emerging discipline, data science broadly means different things across different areas. Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis. Featuring chapters from established authors in both disciplines, the book also presents a number of applications and accompanying papers.

CONTENTS
Preface v
1. Does Data Science Need Statistics? 1
William Oxbury
2. Principled Statistical Inference in Data Science 21
Todd A. Kuffner and G. Alastair Young
3. Evaluating Statistical and Machine Learning Supervised Classification Methods 37
David J. Hand
4. Diversity as a Response to User Preference Uncertainty 55
James Edwards and David Leslie
5. L-kernel Density Estimation for Bayesian Model Selection 69
Mark Briers
6. Bayesian Numerical Methods as a Case Study for Statistical Data Science 99
Fran¸cois-Xavier Briol and Mark Girolami
7. Phylogenetic Gaussian Processes for Bat Echolocation 111
J. P. Meagher, T. Damoulas, K. E. Jones and M. Girolami
8. Reconstruction of Three-Dimensional Porous Media: Statistical or Deep Learning Approach? 125
Lukas Mosser, Thomas Le Bl´evec and Olivier Dubrule
9. Using Data-Driven Uncertainty Quantification to Support Decision Making 141
Charlie Vollmer, Matt Peterson, David J. Stracuzzi and Maximillian G. Chen
10. Blending Data Science and Statistics across Government 155
Owen Abbott, Philip Lee, Matthew Upson, Matthew Gregory and Dawn Duhaney
11. Dynamic Factor Modelling with Spatially Multi-scale Structures for Spatio-temporal Data 167
Takamitsu Araki and Shotaro Akaho
Index 181