Written by a leading researcher with extensive experience in data management
An increasing number of businesses and organizations are creating new analytics departments to identify, manage, and make best use of patterns within their large data repositories. This is the first book to target data mining practitioners working within these organizations, by introducing the key analytical concepts and emerging techniques in data analytics. Each chapter in the book lists the basic results and key facts relevant to the topic, along with a series of exercises and complete solutions. Many of the problems are presented using real case scenario.
Introduction and Classification of Common Analytics problem. From Effect to Cause: Bayes Theorem and Applications. Linear Algebra: A Data Perspective. The Role of Dynamic Programming in Sequential Analysis. The EM framework and customizations. Graph-based techniques for Relational Analysis. Pattern Analysis and Lattice Enumeration. Data Structures for Pattern Analysis.
University of Sydney, Australia.