COMP SCI 7210 - Foundations of Computer Science A

North Terrace Campus - Trimester 3 - 2022

This course will develop your coding and problem-solving skills with a focus on data and data science. You will learn algorithm design as well as fundamental programming concepts such as data, selection, iteration and functional decomposition, data abstraction and organisation. You will build fundamental software development skills including the use of the Python programming language and tools, debugging, testing and fundamentals of good programming practice, style and design.

  • General Course Information
    Course Details
    Course Code COMP SCI 7210
    Course Foundations of Computer Science A
    Coordinating Unit Computer Science
    Term Trimester 3
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 6 hours per week
    Incompatible COMP SCI 7202, COMP SCI 7208, COMP SCI 7103
    Assessment Assignments
    Course Staff

    Course Coordinator: Dr Weitong Chen

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    Upon completion of this course/subject, students will be able to:

    Interpret and decompose problems in computational domains.
    Justify and demonstrate an understanding of programming fundamentals.
    Apply programming fundamental knowledge to practical problems.
    Use the Python programming language to construct programs to solve real-world problems.
    Independently find and interpret discipline-related documentation.
    Explain the benefits of object-oriented design and understand when it is an appropriate methodology to use.
    Design object-oriented solutions for small systems involving multiple objects.
    Translate real-world data to computer representation using different data structures.
    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 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.


    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.

  • Learning Resources
    Recommended Resources
    Zhang, Y., (2015), An Introduction to Python and Computer Programming,(1st ed. Lecture Notes in Engineering 353), Springer, London.

    Lee, K., & Mackie, I., (2014), Python Programming Fundamentals (2nd ed. Undergraduate Topics in Computer Science), Springer, London.

    Matthes, Eric. Python crash course: a hands-on, project-based introduction to programming.

    Phillips, D. (2015). Python 3 object-oriented programming. Packt Publishing Ltd.

    Baka, B. (2017). Python Data Structures and Algorithms. Packt Publishing Ltd.

    Lee, K. D., Lee, K. D., & Steve Hubbard, S. H. (2015). Data Structures and Algorithms with Python. Springer.

    Textbooks are available to students as e-books through the Library.
    Online Learning
    All materials are available from MyUni and it is possible to work through most of the course activities off-site.
    Workshops will be conducted using Zoom for online students.
  • Learning & Teaching Activities
    Learning & Teaching Modes

    No information currently available.


    No information currently available.

    Learning Activities Summary

    No information currently available.

  • 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**
    Programming Practice 0 Individual Formative Weekly 1-6. 1.1 1.2 2.2 2.6 3.1 3.2 3.3 4.1 4.2 4.3
    Module Tests 100 Individual Summative 4, 7, 10 & 13 85% 1-6 1.1 1.2 2.2 2.6 3.1 3.2 3.3 4.1 4.2 4.3
    Total 100
    * The specific due date for each assessment task will be available on MyUni.

    In accordance with the Assessment for Coursework Programs Policy, Procedure 1bAn exemption from the stated hurdle requirements has been granted.

    This course has a hurdle requirement. Meeting the specified hurdle criteria is a requirement for passing the course.

    **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 must complete 4 specific modules as prescribed by your program of study.

    Each module has a hurdle requirement, which is the module test. You need to achieve at least 85% on the module test to pass the module. You will have a limited opportunity to retake module tests that you do not pass in subsequent test weeks but these will be arranged in conjunction with the course coordinator in later testing weeks. If you don’t pass enough of the module tests, you may be required to take any or all of the modules again in a subsequent offering.

    You will be required to demonstrate your ability to apply what you have learnt each week in the creation of programs to solve practice problems to be eligible to sit for the module test.

    Successful completion of an appropriate set of modules will result in a Non-Graded Pass (NGP) in this course.
    Assessment Detail

    No information currently available.


    Submission details and the assignment descriptions will be published on the course website in

    Module Test

    Formative assessments must be completed prior to module test. Module tests are run under exam conditions and late submission is not accepted. You must be available during the replacement examination period (check University dates). If you are offered a replacement examination or additional assessment and are unable to attend for any reason, there may be no further opportunity for a replacement examination or additional assessment.

    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

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