Statistical Data Science
eBook - ePub

Statistical Data Science

  1. 192 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Statistical Data Science

About this book

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

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Yes, you can access Statistical Data Science by Niall Adams, Edward Cohen in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Publisher
WSPC (EUROPE)
Year
2018
eBook ISBN
9781786345417
Topic
Computer Science
Subtopic
Data Mining
Index
Computer Science

Table of contents

  1. Cover
  2. Halftitle
  3. Title
  4. Copyright
  5. Preface
  6. Contents
  7. Chapter 1 Does Data Science Need Statistics?
  8. Chapter 2 Principled Statistical Inference in Data Science
  9. Chapter 3 Evaluating Statistical and Machine Learning Supervised Classification Methods
  10. Chapter 4 Diversity as a Response to User Preference Uncertainty
  11. Chapter 5 L-kernel Density Estimation for Bayesian Model Selection
  12. Chapter 6 Bayesian Numerical Methods as a Case Study for Statistical Data Science
  13. Chapter 7 Phylogenetic Gaussian Processes for Bat Echolocation
  14. Chapter 8 Reconstruction of Three-Dimensional Porous Media: Statistical or Deep Learning Approach?
  15. Chapter 9 Using Data-Driven Uncertainty Quantification to Support Decision Making
  16. Chapter 10 Blending Data Science and Statistics across Government
  17. Chapter 11 Dynamic Factor Modelling with Spatially Multi-scale Structures for Spatio-temporal Data
  18. Index