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000725446 020__ $$a9783319130729$$qelectronic book
000725446 020__ $$a3319130722$$qelectronic book
000725446 020__ $$z9783319130712
000725446 0247_ $$a10.1007/978-3-319-13072-9$$2doi
000725446 035__ $$aSP(OCoLC)ocn902632472
000725446 035__ $$aSP(OCoLC)902632472
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000725446 050_4 $$aQA76.9.D35$$bL44 2015eb
000725446 08204 $$a005.7/3$$223
000725446 1001_ $$aLee, Kent D.$$q(Kent Derek),$$eauthor.
000725446 24510 $$aData structures and algorithms with Python$$h[electronic resource] /$$cKent D. Lee, Steve Hubbard.
000725446 264_1 $$aCham :$$bSpringer,$$c2015.
000725446 300__ $$a1 online resource (xv, 363 pages) :$$billustrations (some color).
000725446 336__ $$atext$$btxt$$2rdacontent
000725446 337__ $$acomputer$$bc$$2rdamedia
000725446 338__ $$aonline resource$$bcr$$2rdacarrier
000725446 4901_ $$aUndergraduate topics in computer science,$$x1863-7310
000725446 504__ $$aIncludes bibliographical references and index.
000725446 5050_ $$aPython Programming 101 -- Computational Complexity -- Recursion -- Sequences -- Sets and Maps -- Trees -- Graphs -- Membership Structures -- Heaps -- Balanced Binary Search Trees -- B-Trees -- Heuristic Search -- Appendix A: Integer Operators -- Appendix B: Float Operators -- Appendix C: String Operators and Methods -- Appendix D: List Operators and Methods -- Appendix E: Dictionary Operators and Methods -- Appendix F: Turtle Methods -- Appendix G: TurtleScreen Methods -- Appendix H: Complete Programs.
000725446 506__ $$aAccess limited to authorized users.
000725446 520__ $$aThis clearly structured and easy to read textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by motivating examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. The text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python. Topics and features: Includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface Provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples Offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author Presents a primer on Python for those coming from a different language background Reviews the use of hashing in sets and maps, along with an examination of binary search trees and tree traversals, and material on depth first search of graphs Discusses topics suitable for an advanced course, such as membership structures, heaps, balanced binary search trees, B-trees and heuristic search Students of computer science will find this clear and concise textbook to be invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.
000725446 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 2, 2015).
000725446 650_0 $$aData structures (Computer science)
000725446 650_0 $$aPython (Computer program language)
000725446 650_0 $$aComputer algorithms.
000725446 7001_ $$aHubbard, Steve,$$eauthor.
000725446 77608 $$iPrint version:$$z9783319130712
000725446 830_0 $$aUndergraduate topics in computer science.
000725446 852__ $$bebk
000725446 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-13072-9$$zOnline Access$$91397441.1
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000725446 980__ $$aEBOOK
000725446 980__ $$aBIB
000725446 982__ $$aEbook
000725446 983__ $$aOnline
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