Chen L.M., Su Z., Jiang B. Mathematical Problems in Data Science

John Wiley & Sons, Inc., 2017. — 416 p. — ISBN: 978-1119092940. A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline. Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good...

Springer, 2016. — 219 p. — ISBN: 3319251252, 9783319251257 This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For...

Springer Science+Business Media, 2009. — 786 p. — ISBN: 978-0387981345, e-ISBN: 978-0387981352. This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus...

Academic Press, 2009. — 864 p. — ISBN: 0123747651. Robert Nisbet, Pacific Capital Bank Corporation, Santa Barbara, CA, USA John Elder, Elder Research, Inc. and the University of Virginia, Charlottesville, USA Gary Miner, StatSoft, Inc. , Tulsa, OK, USA Description The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book...

Apress, 2018. — 821 p. — ISBN: 1484230531. Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen...

2nd edition. — Elsevier, 2005. Features Explains how data mining algorithms work. Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques. Covers performance improvement techniques, including input preprocessing and combining output from different methods. Shows you how to use the Weka machine learning...