Statistical Data Science

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:

• Does Data Science Need Statistics? (William Oxbury)
• Principled Statistical Inference in Data Science (Todd A Kuffner and G Alastair Young)
• Evaluating Statistical and Machine Learning Supervised Classification Methods (David J Hand)
• Diversity as a Response to User Preference Uncertainty (James Edwards and David Leslie)
• L-kernel Density Estimation for Bayesian Model Selection (Mark Briers)
• Bayesian Numerical Methods as a Case Study for Statistical Data Science (François-Xavier Briol and Mark Girolami)
• Phylogenetic Gaussian Processes for Bat Echolocation (J P Meagher, T Damoulas, K E Jones and M Girolami)
• Reconstruction of Three-Dimensional Porous Media: Statistical or Deep Learning Approach? (Lukas Mosser, Thomas Le Blévec and Olivier Dubrule)
• Using Data-Driven Uncertainty Quantification to Support Decision Making (Charlie Vollmer, Matt Peterson, David J Stracuzzi and Maximillian G Chen)
• Blending Data Science and Statistics Across Government (Owen Abbott, Philip Lee, Matthew Upson, Matthew Gregory and Dawn Duhaney)
• Dynamic Factor Modeling with Spatially Multi-scale Structures for Spatio-temporal Data (Takamitsu Araki and Shotaro Akaho)

Readership: Statisticians, mathematicians, computer scientists, data scientists, application users of data science and statistics.
Key Features:

• Detailed papers by authors from both Statistics and Data Science
• Exploration of similarities and differences between disciplines
• Application papers which feature both Data Science and Statistics