About this book series

Springer Series in the Data Sciences focuses primarily on monographs and graduate level textbooks. The target audience includes students and researchers working in and across the fields of mathematics, theoretical computer science, and statistics. Data Analysis and Interpretation is a broad field encompassing some of the fastest-growing subjects in interdisciplinary statistics, mathematics and computer science. It encompasses a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, including diverse techniques under a variety of names, in different business, science, and social science domains. Springer Series in the Data Sciences addresses the needs of a broad spectrum of scientists and students who are utilizing quantitative methods in their daily research. The series is broad but structured, including topics within all core areas of the data sciences. The breadth of the series reflects the variation of scholarly projects currently underway in the field of machine learning.
Electronic ISSN
Print ISSN
Series Editor
  • David Banks,
  • Jianqing Fan,
  • Michael Jordan,
  • Ravi Kannan,
  • Yurii Nesterov,
  • Christopher Ré,
  • Ryan Tibshirani,
  • Larry Wasserman

Book titles in this series

Abstracted and indexed in

  1. zbMATH