Mathematical Problems in Data Science

“Data science includes mathematical and statistical tools required to find relations and principles behind heterogeneous and possibly unstructured data. It is an emerging field, under active research, and the authors here have attempted to explain existing methods whole introducing some open problems. … Overall, the book offers a collection of papers that describe current trends and future directions along with appropriate references. The presented applications cover a broad spectrum of domains where big data poses challenges.” (Paparao Kayalipati, Computing Reviews, computingreviews.com, September, 2016)

• Introduction: Data Science and BigData Computing.- Overview of Basic Methods for Data Science.- Relationship and Connectivity of Incomplete Data Collection.- Machine Learning for Data Science: Mathematical or Computational.- Images, Videos, and BigData.- Topological Data Analysis.- Monte Carlo Methods and their Applications in Big Data Analysis.- Feature Extraction via Vector Bundle Learning.- Curve Interpolation and Financial Curve Construction.- Advanced Methods in Variational Learning: Segmentation with Intensity Inhomogeneity.- An On-line Strategy of Groups Evacuation From a Convex Region in the Plane.- A New Computational Model of Bigdata.