COMP SCI 2201 - Algorithm & Data Structure Analysis

North Terrace Campus - Semester 2 - 2015

Program development techniques including basic ideas of correctness and proof; Notions of complexity and analysis; Recursion. Approaches to Problem Solving. Notion of abstract data type, representation of lists, stacks, queues, sets, trees and hash tables. Graphs and Graph Traversal.

  • General Course Information
    Course Details
    Course Code COMP SCI 2201
    Course Algorithm & Data Structure Analysis
    Coordinating Unit Computer Science
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites One of COMP SCI 1007, COMP SCI 1009, COMP SCI 1103, COMP SCI 1203, COMP SCI 2103 or COMP SCI 2202
    Incompatible COMP SCI 2004
    Assessment Written exam and/or assignments.
    Course Staff

    Course Coordinator: Dr Mingyu Guo

    Lecturer: external
    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    1. Skills in performing analysis of given recursive and iterative algorithms.
    2. Understanding and performing simple proofs of algorithmic complexity and correctness.
    3. An ability to understand and derive recurrences describing algorithms and properties of data structures.
    4. An understanding of the implementation and efficiency of a range of data structures including, trees, binary heaps, hash-tables and graphs.
    5. An understanding of a variety of well-known algorithms on some of the data structures presented.
    6. The ability to implement and use these algorithms in code.
    7. A foundational understanding of intractability. An understanding of proof techniques for NP- Completeness.
    8. An ability to solve new analytic and algorithmic problems.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1-8
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1,8
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 1,3,6,7,8
    A proficiency in the appropriate use of contemporary technologies. 2,6,7
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1,8
    A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 1,2,7,8
  • Learning Resources
    Required Resources
    The textbook for this course is Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Third Edition, MIT Press.
    Recommended Resources
    Recommended further reading: 
    Algorithms and Data Structures - The Basic Toolbox by Kurt Mehlhorn and Peter Sanders, Springer, 2008.(the full text is available on the Author’s website).
    Online Learning
    Course Website:
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Lectures and Tutorials. Most tutorials will be handed in for assessment.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements. 

    The workload is approximately 12 hours per week during semester time. This consists of an average of 2.5 hours of contact time and the remaining time for study and working on tutorial submissions.
    Learning Activities Summary
    • The following details the topics to be introduced by the lectures
    • The tutorial topics will broadly follow this schedule 
    • IntroductionInteger-Arithmetics
    • Recursive-Multiplication
    • Karatsuba-Multiplication 
    • Binary-Search-Trees
    • Binary-Search-Invariants
    • Priority-Queues
    • PQ-HeapSort-Binary-Search-Trees 
    • BST-average-case
    • AVL-TreesSkip-Lists
    • Hashing1
    • Hashing2
    • Graphs
    • Graph-Representations-BFS
    • DFS-Connected-Components
    • Shortest-Paths1
    • Shortest-Paths2
    • DynamicProgramming
    • Minimum-Spanning-Trees
    • Minimum-Spanning-Trees2 
    • P-and-NP
    • P-and-NP2
    • Exam-Preparation
    Small Group Discovery Activities will start in week 3.
    Specific Course Requirements
    There are no specific requirements for this course beyond prerequisite knowledge and the ability to attend the lectures and tutorials.
    Small Group Discovery Experience
    There is a small group discovery experience component that is worth 5% of your final mark.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    The course assessment consists of three components: 
    • A written exam worth 70% of the marks for the course 
    • Written submissions to tutorials (some, optionally, done in teams) 25% of the marks for the course.
    • Small group discovery experience, 5% of the marks for the course.

    Below are the CBOK mappings:

    Component  Abstraction  Design  Interpersonal Communication  Programming
    Lecture x x x
    Tutorials 3 5 3 5
    Exam 3 4 3

    Details of the Australian Computer Society's Core Bode of Knowledge (CBOK) can be found in

    Below are the mappings to learning outcomes and graduate attributes:

    Component  Weight  Learning Outcomes  Graduate Attributes
    Tutorials  25%  1,3,4,5,6,8  1,2,3,4,5,6
    Exam  70%  1,2,3,4,5,6,7,8  1,2,3,4,5
    SGDE  5%  4,5,6,8  1,2,3,4,5,6
    Assessment Related Requirements
    You are also encouraged to attend the tutorial sessions. Application for exemptions based on medical and/or compassionate grounds must be made to the course coordinator.
    Assessment Detail
    The written exam will be centrally administered by examinations and held at the end of semester. 

    Each tutorial will be based on materials presented at that stage of the course or on readings drawn from reference materials. 

    Three out of the six tutorials will be assessed with each tutorial being worth 5-10% of the course mark. Some tutorials will be based on group work.

    Tutorials will be marked within one and a half weeks of the tutorial submission deadline. Brief written feedback will be provided along with marks.

    Marks for SGDE will be based on participation and preparation for the exercises.
    Details of the submission of tutorials will be written on each tutorial handout. The submission time for tutorials will usually be only one day prior to the first tutorial presentation. As such no late submission is accepted unless prior arrangement is made with the course coordinator for an extension on medical or compassionate grounds.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.