Originally published in 2001, reissued as part of Pearson's modern classic series.

Discrete Mathematical Structures, Sixth Edition, offers a clear and concise presentation of the fundamental concepts of discrete mathematics. Ideal for a one-semester introductory course, this text contains more genuine computer science applications than any other text in the field. This book is written at an appropriate level for a wide variety of majors and non-majors, and assumes a college algebra course as a prerequisite.

This text emphasizes the physical interpretation of mathematical solutions and introduces applied mathematics while presenting differential equations. Coverage includes Fourier series, orthogonal functions, boundary value problems, Green’s functions, and transform methods. This text is ideal for students in science, engineering, and applied mathematics.

This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis-from wrangling data and generating statistics to identifying patterns.

Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. Along with hands-on exercises and real-life case st...