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Course Outlines

COMP SCI 2201 - Algorithm & Data Structure Analysis

North Terrace Campus - Semester 1 - 2024

This course provides an introduction to program development techniques with a focus on basic ideas of correctness and proof. The course introduces, among others, notions of complexity and analysis, recursion, abstract data types, representation of lists, stacks, queues, sets, trees and hash tables, graphs and Graph Traversal. The course allows students to experience different approaches to problem solving.

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  • General Course Information
    Course Details
    Course Code COMP SCI 2201
    Course Algorithm & Data Structure Analysis
    Coordinating Unit Computer Science
    Term Semester 1
    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 1103, COMP SCI 1203, COMP SCI 2103, COMP SCI 2202 or COMP SCI 2202B
    Incompatible COMP SCI 2004
    Assessment Written exam and/or assignments.
    Course Staff

    Course Coordinator: Dr Muhammad Uzair

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will gain:

     
    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)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    		 1-8

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    		 1,2,8

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

    		 1-8

    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

    		 1,6,8

    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.

    		 8

    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

    		 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).
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will primarily utilize three activities to deliver the content:

    Lectures: Lecture sessions will introduce and explain the foundational concepts that form the basis of algorithm and data structure analysis.
    Lecture sessions will provide an in-depth coverage of topics related to time and space complexity, algorithm and data structure analysis techniques, and algorithmic proof and correctness. Interactive discussions will be encouraged to enhance the learning experience and promote a deeper comprehension of the subject matter.

    Workshop Sessions: In workshop sessions, students will collaboratively solve problem sets that require algorithm and data structure analysis. Tutors will provide guidance in overcoming challenges and optimizing problem-solving approaches. Through group work and solution discussions, students will enhance their problem-solving skills, reinforce their understanding of algorithm analysis, and develop the ability to evaluate solutions for efficiency and correctness.

    Assignments: Assignments will reinforce the concepts learned and foster problem-solving skills. Students will be given problem-solving assignments that require the application of algorithm and data structure. Assignments will strengthen students' ability to apply algorithm and data structure analysis to real-world problems.
    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
    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
  • 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
    Assessment Task Weighting (%) Individual/ Group Formative/ Summative
    		Due (week)*
    		 Hurdle criteria Learning outcomes CBOK Alignment**
    Assignments 35 Individual Summative Weeks 2-12 1. 2. 3. 4. 5. 6. 7. 8. 1.1 1.2 4.1
    Exam 60 Individual Summative NA min 40% 1. 3. 4. 5. 6. 8. 1.1 1.2 4.1
    SGDE 5 Group Summative Week 12 4. 5. 6. 8. 2.3 2.4
    Total 100
    * The specific due date for each assessment task will be available on MyUni.
     
    This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.

    **CBOK is the Core Body of Knowledge for ICT Professionals defined by the Australian Computer Society. The alignment in the table above corresponds with the following CBOK Areas:

    1. Problem Solving
    1.1 Abstraction
    1.2 Design

    2. Professional Knowledge
    2.1 Ethics
    2.2 Professional expectations
    2.3 Teamwork concepts & issues
    2.4 Interpersonal communications
    2.5 Societal issues
    2.6 Understanding of ICT profession

    3. Technology resources
    3.1 Hardware & Software
    3.2 Data & information
    3.3 Networking

    4. Technology Building
    4.1 Programming
    4.2 Human factors
    4.3 Systems development
    4.4 Systems acquisition

    5.  ICT Management
    5.1 IT governance & organisational
    5.2 IT project management
    5.3 Service management 
    5.4 Security management
    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
    Assignments in this course consist of programming tasks that are related to the topics covered. These assignments are intended to be completed individually.
    Submission
    Submissions will be done online through either MyUni or the web submission system.
    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 .

  • 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

    This section contains links to relevant assessment-related policies and guidelines - all university policies.

  • 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鈥檚 disciplinary procedures.

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