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COMP 352 Data Structures and Algorithms
创建日期：2024-06-27 17:45:06
标签： COMP 352
Data Structures and Algorithms

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COMP 352 Data Structures and Algorithms

1. Course Description

Topics: Abstract data types: stacks and queues, trees, priority queues, dictionaries. Data structures:
arrays, linked lists, heaps, hash tables, search trees. Design and analysis of algorithms: asymptotic
notation, recursive algorithms, searching and sorting, tree traversal, graph algorithms.
2. Learning Objectives
• Knowledge base: Demonstrate competence in fundamentals of data structures and
algorithms.
• Problem analysis: Analyze and state model limitations and elements of uncertainty.
Formulate and calculate qualitative and quantitative qualities of the problems’ inputs and
outputs. Estimate computational complexity. Evaluate and pick the most appropriate approach
based on relevant criteria.
• Design:

– Critique/evaluate many possible diverse solutions and use techniques to evaluate different
solutions with sound arguments related to the problems’ requirements and constraints.
Demonstrate thinking outside the box to create innovative solutions.
– Develop a system architecture adapted to the systems application context and its
requirements and constraints. Development and specification of internal and external
software interfaces at different modularity levels. Describe a solution that presents
enough details for implementation.
– Write code according to design. Validate implemented systems against system
requirements, specifications and constraints, as well as interface specifications.
• Use of Engineering tools: Demonstrate appropriate operational use of tools (e.g.,
algorithms, abstract data types, data structures, asymptotic complexity analysis) for specific
tasks in a laboratory environment.
• One credit represents, for an average student, a minimum of 45 hours of workload spread
across the various academic activities (Source: Article 16.1.2 of the Undergraduate Calendar.)
For an average student, this suggests a minimum of 135 hours of workload for a 3-credit
course, including the time spent in lectures, tutorials, laboratories, examinations, and
personal and team work.

3. Course Materials

Textbook M.T. Goodrich, R. Tamassia, Michael H. Goldwasser. Data Structures and Algorithms
in Java, 6th edition. John Wiley & Sons, 2014. ISBN 978-1-118-77133-4. (Note: 5th edition is ok.)
The book is available at the bookstore or can be rented as eTextbook.
From the textbook we shall study these sections: 3.1 to 3.4, 4.1 to 4.3, 5.1 to 5.6, 6.1 to 6.2, 7.1 to 7.3,
8.1 to 8.4, 9.1 to 9.4, 10.1 to 10.3, 11.1 to 11.3, 12.1 to 12.4, 14.1 to 14.7, and 15.1 to 15.2. You also need to
study chapters 1 and 2 that will not be covered in provided lectures.

Web Page Many resources for the course (slides, assignments, example programs, …) will be
available online through Moodle; , or through the instructor’s website. Your instructor will inform you of the
exact link to access the materials.
In specific, depending on your section, the lectures will either take place live over Zoom (where
coverage of the material will take place during class time), or they will be pre-recorded. Your
instructor will inform you about the way the class will be conducted. In case the lectures are pre-
recorded, you will need to view these video lectures before class time. In such case, only discussions
about the covered materials will take place during class time, where you will be able to get
clarifications to any questions that you may have.
All tutorials will be available online over Moodle/Yuja (accessible from Moodle) as video lectures
prior to tutorial time. You will need to view these videos before tutorial time. While the tutors may
very well elaborate further on the materials discussed on the videos, you need to view these video
recordings ahead of tutorial time.

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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