Data science is a new discipline that incorporates automated ways to examine patterns and models for all types of data, with applications ranging from scientific discovery to business intelligence and analytics. Its fundamental algorithms in data mining and analysis serve as its foundation.

With the integration of relevant ideas from statistics and machine learning, this textbook for senior undergraduate and graduate data mining courses offers a thorough and comprehensive overview of data mining. Exploratory data analysis, pattern mining, clustering, and classification are some of the book's primary sections.

The book includes cutting-edge topics such as kernel approaches, high-dimensional data analysis, complicated graphs, and networks in addition to outlining the fundamentals of these activities.

This book provides sound advice in data mining for students, academics, and practitioners alike thanks to its thorough covering, algorithmic approach, and richness of examples. Important characteristics: Covers both traditional methods and contemporary research * Algorithmic approach with open-source implementations * Few pre-requisites: all fundamental mathematical ideas are covered, as well as the reasoning behind the formulas * Short, self-contained chapters with class-tested examples and exercises, allow for flexibility in course design and for easy reference * Additional website featuring project ideas, videos, and teaching slides.