This edited volume on data science features a variety of research ranging from theoretical to
applied and computational topics. Aiming to establish the important connection between
mathematics and data science this book addresses cutting edge problems in predictive modeling
multi-scale representation and feature selection statistical and topological learning and
related areas. Contributions study topics such as the hubness phenomenon in high-dimensional
spaces the use of a heuristic framework for testing the multi-manifold hypothesis for
high-dimensional data the investigation of interdisciplinary approaches to multi-dimensional
obstructive sleep apnea patient data and the inference of a dyadic measure and its simplicial
geometry from binary feature data. Based on the first Women in Data Science and Mathematics
(WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for
Compuational and Experimental Research in Mathematics (ICERM) in Providence Rhode Island this
volume features submissions from several of the working groups as well as contributions from
the wider community. The volume is suitable for researchers in data science in industry and
academia.
