000 04092nam a22005055i 4500
001 978-3-319-13072-9
003 DE-He213
005 20200421111700.0
007 cr nn 008mamaa
008 150112s2015 gw | s |||| 0|eng d
020 _a9783319130729
_9978-3-319-13072-9
024 7 _a10.1007/978-3-319-13072-9
_2doi
050 4 _aQA76.9.D35
072 7 _aUMB
_2bicssc
072 7 _aCOM062000
_2bisacsh
082 0 4 _a005.73
_223
100 1 _aLee, Kent D.
_eauthor.
245 1 0 _aData Structures and Algorithms with Python
_h[electronic resource] /
_cby Kent D. Lee, Steve Hubbard.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXV, 363 p. 147 illus., 139 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUndergraduate Topics in Computer Science,
_x1863-7310
505 0 _a1: Python Programming 101 -- 2: Computational Complexity -- 3: Recursion -- Sequences -- 4: Sets and Maps -- 5: Trees -- 6: Graphs -- 7: Membership Structures -- 8: Heaps -- 9: Balanced Binary Search Trees -- 10: B-Trees -- 11: 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.
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.
650 0 _aComputer science.
650 0 _aComputer programming.
650 0 _aData structures (Computer science).
650 0 _aAlgorithms.
650 1 4 _aComputer Science.
650 2 4 _aData Structures.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aProgramming Techniques.
700 1 _aHubbard, Steve.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319130712
830 0 _aUndergraduate Topics in Computer Science,
_x1863-7310
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-13072-9
912 _aZDB-2-SCS
942 _cEBK
999 _c54930
_d54930