COMP SCI 2201 - Algorithm & Data Structure Analysis

North Terrace Campus - Semester 2 - 2017

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 School of 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
    Course Description 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.
    Course Staff

    Course Coordinator: Dr Mingyu Guo

    Co-lecturer: Ali Shemshadi
    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1-8
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    1,2,8
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    1-8
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1,6,8
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • Able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
    8
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, diffuse conflict and engage positively in purposeful debate
    1,2,5,6,8
  • Learning Resources
    Required Resources
    Textbook 
    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: 
    1. 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).
    2. Data Structures and Algorithms in Java by Michael T. Goodrich, Irvine Roberto Tamassia, and Michael H. Goldwasser, Wiley, 6th Edition, 2014. (available in the library).
    Online Learning
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Lectures, tutorials, and small group discovery experience (SGDE) activities.
    Workload

    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.

    • Introduction to complexity of algorithms, asymptotic notations
    • Integer arithmetic
    • Recursive and Karatsuba multiplication
    • Priority queues and heaps
    • Linear-time sorting algorithms
    • Binary search trees and average case analysis
    • AVL trees and skip-lists
    • Hashing and hash tables
    • Graphs and their representations
    • Breadth-first-search and depth-first-search   
    • Strongly connected components
    • Shortest path problem
    • Dynamic programming
    • Minimum spanning trees
    • Complexity classes: P versus NP
    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, tutorials, and SGDE activities.
    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, 70% of the marks for the course;
    • written submissions to assignments (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 Weighting CBOK Areas
    Final written exam 70% 1,2,8
    Assignments 25% 1,2,4,7,8,9,11
    SGDE 5% 1,2,4,7,8,9,11

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


    Below are the mappings to learning outcomes and graduate attributes:

    Component Weight Learning Outcomes Graduate Attributes
    Final written exam 70% 1,2,3,4,5,6,7,8 1,2,3,4,5
    Assignments 25% 1,3,4,5,6,8 1,2,3,4,5,6
    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 assignment will be based on materials presented at that stage of the course and on readings drawn from reference materials. Three assignments will be given; each being worth 5-10% of the course mark. Some assignments will be based on group work. Assignments will be marked within two weeks after a submission deadline. Brief written feedback will be provided along with marks. Marks for SGDE will be based on participation and preparation for the exercises.


    Submission
    All program code based assignments must be submitted using the School of Computer Science online Submission System. All hand written assignments must be submitted using the School of Computer Science boxes for assignments. Details are included in each assignment description on the course forum. The University policy on plagiarism applies on all submissions.

    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 (http://www.adelaide.edu.au/policies/101/) 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.