Problem Solving with Algorithms and Data Structures Using Python
by Brad Miller, David Ranum
Publisher: Franklin, Beedle & Associates 2011
Number of pages: 438
This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. We cover abstract data types and data structures, writing algorithms, and solving problems.
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by David M. Mount - University of Maryland
The focus is on how to design good algorithms, and how to analyze their efficiency. The text covers some preliminary material, optimization algorithms, graph algorithms, minimum spanning trees, shortest paths, network flows and computational geometry.
by Anil K. Jain, Richard C. Dubes - Prentice Hall
The book is useful for scientists who gather data and seek tools for analyzing and interpreting data. It will be a reference for scientists in a variety of disciplines and can serve as a textbook for a graduate course in exploratory data analysis.
by Jianer Chen
The author concentrates on four themes in computational geometry: the construction of convex hulls, proximity problems, searching problems and intersection problems. Solving manufacturing problems requires application of fast-algorithm techniques.
by Granville Barnett, Luca Del Tongo - DotNetSlackers
The book provides implementations of common and uncommon algorithms in pseudocode which is language independent and provides for easy porting to most programming languages. We assume that the reader is familiar with the object oriented concepts.