University Calendar 2022-2023 | CPSC 2302: Data Structures and Algorithms      1
                                                                                       Constructors, inheritance, polymorphism, exceptions, interfaces,
          CPSC 2302: DATA
                                                                                       generic types, casting and design patterns 3.  Concrete Data
                                                                                       Structures An overview of data structures Concrete vs. abstract
          STRUCTURES AND                                                               data structures, linear vs. non-linear data structures Fundamental
                                                                                       concrete data structures in Java: arrays and vectors, linked lists and
          ALGORITHMS                                                                   trees 4.  Object-Oriented Design and Abstract Data Types Inheritance
                                                                                       Issues in object-oriented program design Basic abstract data types:
                                                                                       arrays, vectors, sets, lists, stacks, queues, maps, tables, dictionaries
          KPU Course Outline of Record
                                                                                       5.  Search Structures and Searching Algorithms Complexity analysis
            • Academic Level: (UG) - Undergraduate (UG)
                                                                                       and big-O notation Linear and binary search tables Hashing, linear-
            • Subject Code: (CPSC) - CPSC - Computer Science                           probed hash tables, chained hash tables and collision resolution
                                                                                       strategies Binary search trees, AVL trees Heaps, priority queues 6. 
            • Course Number: 2302
                                                                                       Sorting Algorithms Insertion and selection sort Quicksort (average
            • Descriptive Title : Data Structures and Algorithms
                                                                                       and worst case analysis) Treesort and heapsort 7.  External Searching
            • Short Title: Data Structures and Algorithms
                                                                                       and Sorting Searching on disk: M-way search trees, B-trees Sorting
            • CIP Code: (110103) - Information technology
                                                                                       on disk - Mergesort 8.  Graphs Adjacent matrix and adjacency list
            • Fee Category 2.a.1?:
                                                                                       representation Depth-first and breadth-first search, topological sorting
            • Differential Fee Category?:                                              Spanning trees Shortest paths ?
            • Calendar Description: Students will learn fundamental tools of data    • Course Learning Activities:
              and program organization including object-oriented programming,
                                                                                     • Mastery Criteria:
              algorithms, data abstraction and data structures.They will learn to
                                                                                     • Assessment Type 1: Assignments and Project
              implement and to use data structures such Lists, Stacks, Queues,
                                                                                     •   • Type 1 Value: 30%
              Trees, Hash Tables, and Graphs. Students will learn algorithms
                                                                                     • Assessment Type 2: Labs
              for tasks including searching and sorting. They will learn to use
                                                                                     •   • Type 2 Value: 10%
              mathematical tools for analyzing algorithm efficiency.
                                                                                     • Assessment Type 3: Quizzes
            • Credits: 3
                                                                                     •   • Type 3 Value: 10%
            • Lecture Hours: 3-4
                                                                                     • Assessment Type 4: Midterm Examination
            • Lab Hours: 0
                                                                                     •   • Type 4 Value: 20%
            • Other Hours: 0
                                                                                     • Assessment Type 5: Final Examination
            • Contact Hours : 3-4
                                                                                     •   • Type 5 Value: 30%
            • Is this course repeatable for additional credit?: No
                                                                                     • Assessment Type 6:
            • Cross-listed Courses:
                                                                                     •   • Type 6 Value:
            • Equivalent Courses: (INFO 2315) - Data Structure
                                                                                     • Assessment Type 7:
            • Credit-exclusion Courses:
                                                                                     •   • Type 7 Value:
            • Optional Calendar Description Note:
                                                                                     • Additional Notes: ??
            • Prerequisites: CPSC 1204 or INFO 2313 Attributes: QUAN (http://
              www.kpu.ca/calendar/2017-18/courses/quantitative.html)                 • Grading System - default: (N) - Letter Grades (N)
            • Corequisites:                                                          • Alternate Grading System(s) - not default:
            • Pathway to Undergraduate Studies:                                      • Methods for Prior Learning Assessment:
            • Degree Requirement Attributes: (QUAN) - Quantitative                   • Required Learning Resources: ? ?The following text or equivalent is
                                                                                       required: F. Carrano W. Savitch. Data Structures and  Abstractions
            • Course Learning Outcomes:
                                                                                       with Java, Prentice Hall, Latest Edition.   or Liang,  Y. Daniel. ,
              1 - Explain the concept of data abstraction and apply it to software
                                                                                       Introduction to Java Programming Comprehensive Version, Pearson.
              design and implementation
                                                                                       Latest Edition. ? This course must be taught in a computer lab?.?
              2 - Select and apply the appropriate data structures for a number of
              problems requiring computer implementation                             • Recommended Learning Resources:
              3 - Use an object-oriented programming language to implement data
                                                                                     • Other Course Materials:
              structures such as arrays, vectors, queues, stacks, lists, hash tables
                                                                                     • Open Educational Resources (OER):
              and trees
                                                                                     • Eligible for Zero Textbook Cost (ZTC)?:
              4 - Implement appropriate algorithms for the given problem
                                                                                     • Does this course require the use of animals?:
              5 - Analyze the implementation of data structures using complexity
              analysis of algorithms using big-O notation                            • Is this course externally accredited?:
            • Content will include, but is not restricted to, the following:: 1.     • External Accrediting Body:
              Software Engineering Principles Basic concepts of software life cycle
                                                                                   This course outline is the official approved version for the Academic
              Modules and modular design Top-down design and object-oriented
                                                                                   Year shown above. Details of the Course Content, Course Learning
              design principles 2.  Data Abstraction and Java Classes Abstract
                                                                                   Activities, Assessment Plans, and Required/Optional Learning Resources
              data types and Java classes Accessibility and information hiding
                                                                                   may vary by individual section in compliance with KPU's Academic
                                                                                   policies. Although every effort is made to ensure accuracy at the time of
                                                                                   publication, KPU reserves the right to make corrections and provisions
     2     University Calendar 2022-2023 | CPSC 2302: Data Structures and Algorithms
     without notice. For further information, see kpu.ca/calendar (https://
     kpu.ca/calendar/).