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.

Table of Contents

Chapter 1. Does Data Science Need Statistics?
Chapter 2. Principled Statistical Inference In Data Science
Chapter 3. Evaluating Statistical And Machine Learning Supervised Classification Methods
Chapter 4. Diversity As A Response To User Preference Uncertainty
Chapter 5. L-Kernel Density Estimation For Bayesian Model Selection
Chapter 6. Bayesian Numerical Methods As A Case Study For Statistical Data Science
Chapter 7. Phylogenetic Gaussian Processes For Bat Echolocation
Chapter 8. Reconstruction Of Three-Dimensional Porous Media: Statistical Or Deep Learning Approach?
Chapter 9. Using Data-Driven Uncertainty Quantification To Support Decision Making
Chapter 10. Blending Data Science And Statistics Across Government
Chapter 11. Dynamic Factor Modelling With Spatially Multi-Scale Structures For Spatio-Temporal Data