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 Clifford A. Shaffer - Dover Publications
A comprehensive treatment focusing on efficient data structures and algorithms, this text explains how to select or design the data structure best suited to specific problems. It uses C++ programming language and is suitable for second-year courses.
by D. P. Williamson, D. B. Shmoys - Cambridge University Press
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. It is organized around techniques for designing approximation algorithms, including greedy and local search algorithms.